Elucidation of genetic determinants of dyslipidaemia using a global screening array for the early detection of coronary artery disease.

Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene-gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD.

Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism.

INTRODUCTION: Tandem mass spectrometry (TMS) has emerged an important screening tool for various metabolic disorders in newborns. However, there is inherent risk of false positive outcomes. Objective To establish analyte-specific cutoffs in TMS by integrating metabolomics and genomics data to avoid false positivity and false negativity and improve its clinical utility. METHODS: TMS was performed on 572 healthy and 3000 referred newborns. Urine organic acid analysis identified 23 types of inborn errors in 99 referred newborns. Whole exome sequencing was performed in 30 positive cases. The impact of physiological changes such as age, gender, and birthweight on various analytes was explored in healthy newborns. Machine learning tools were used to integrate demographic data with metabolomics and genomics data to establish disease-specific cut-offs; identify primary and secondary markers; build classification and regression trees (CART) for better differential diagnosis; for pathway modeling. RESULTS: This integration helped in differentiating B12 deficiency from methylmalonic acidemia (MMA) and propionic acidemia (Phi coefficient=0.93); differentiating transient tyrosinemia from tyrosinemia type 1 (Phi coefficient=1.00); getting clues about the possible molecular defect in MMA to initiate appropriate intervention (Phi coefficient=1.00); to link pathogenicity scores with metabolomics profile in tyrosinemia (r2=0.92). CART model helped in establishing differential diagnosis of urea cycle disorders (Phi coefficient=1.00). CONCLUSION: Calibrated cut-offs of different analytes in TMS and machine learning-based establishment of disease-specific thresholds of these markers through integrated OMICS have helped in improved differential diagnosis with significant reduction of the false positivity and false negativity rates.

Pharmacogenetic profiling of dihydropyrimidine dehydrogenase (DPYD) variants in the Indian population.

BACKGROUND: The present study aimed to delineate the pharmacologically relevant dihydropyrimidine dehydrogenase (DPYD) variants in the Indian population. METHODS: We screened 2000 Indian subjects for DPYD variants using the Infinium Global Screening Array (GSA) (Illumina Inc., San Diego, CA, USA). RESULTS: The GSA analysis identified seven coding, two intronic and three synonymous DPYD variants. Level 1A alleles (rs75017182, rs3918290, P633Qfs*5 and D949V) were found to be rare (minor allele frequency: 1.889%), whereas Level 3 alleles were observed to be predominant (C29R: 24.91%, I543V: 9.047%, M166V: 8.993% and V732I: 8.44%). In silico predictions revealed that all Level 1A alleles were deleterious, whereas three (M166V, S534N and V732I) of seven Level 3 alleles were damaging. CUPSAT analysis revealed that two Level 1A (P633Qfs*, D949V) and three Level 3 (I543V, V732I and S534N) variants were thermolabile. The pooled Indian data showed that V732I, S534N and rs3918290 variants were associated with 5-FU/capecitabine toxicity, whereas C29R, I543V and M166V variants exhibited the null association. A comparison of our data with other population data from the 'Allele Frequency Aggregator' (https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/) database showed similarities with the South Asian data. CONCLUSIONS: We have identified four Level 1A (non-functional/dysfunctional) and seven Level 3 variants in the DPYD gene. The pooled Indian data revealed the association of V732I, S534N and rs3918290 variants with 5-FU/capecitabine toxicity. Clustering analysis revealed the similarities in the DPYD profiles of the Indian and South Asian populations.

Influence of RFC1 c.80A>G Polymorphism on Methotrexate-Mediated Toxicity and Therapeutic Efficacy in Rheumatoid Arthritis: A Meta-analysis.

BACKGROUND: Methotrexate (MTX) is an antirheumatic drug, transported by reduced folate carrier-1 (RFC1). The most common RFC1 gene variant, c.80 A>G (rs1051266) is ambiguously linked to adverse effects of MTX therapy in some rheumatoid arthritis (RA) patients. OBJECTIVE: The purpose of meta-analysis was to summarize all major published studies on c.80 A>G SNP to clarify this ambiguity in MTX therapy. METHODS: A total of 18 studies representing 3592 RA patients comprising 699 men and 2893 women were included. Both fixed and random effect models were applied to study the data. RESULTS: The RFC1 80A-allele showed null association with MTX-mediated toxicity in both fixed (odds ratio [OR] = 0.91; 95% CI = 0.80-1.03) and random effects (OR = 0.89; 95% CI: 0.71-1.11) models. Because heterogeneity was observed in this association (P = 0.0006), data were segregated based on use of folate therapy. In 7 studies (n = 1191) where folate was used along with MTX, RFC1 AA patients showed reduced risk for MTX-mediated toxicity (OR = 0.67; 95% CI: 0.50-0.89; P = 0.0006). The RFC1 80A-allele was found to increase the efficacy of MTX therapy by 1.53-fold (95% CI: 1.24-1.88), whereas the 80AA-genotype increased the efficacy by 1.85-fold (95% CI: 1.41-2.42). No publication bias was observed in these associations. CONCLUSION AND RELEVANCE: RFC1 c.80 A>G is an important pharmacogenetic determinant of MTX therapy in RA. The RFC1 80A-allele robustly increased therapeutic efficacy and safety when folate was used along with MTX. Findings are relevant to decision-making in the clinical use of MTX as a treatment for RA patients harboring the RFC1 gene variant.

Probing the epigenetic signatures in subjects with coronary artery disease.

Depletion of S-adenosyl methionine and 5-methyltetrahydrofolate; and elevation of total plasma homocysteine were documented in CAD patients, which might modulate the gene-specific methylation status and alter their expression. In this study, we have aimed to delineate CAD-specific epigenetic signatures by investigating the methylation and expression of 11 candidate genes i.e. ABCG1, LIPC, PLTP, IL-6, TNF-α, CDKN2A, CDKN2B, F2RL3, FGF2, P66 and TGFBR3. The methylation-specific PCR and qRT-PCR were used to assess the methylation status and the expression of candidate genes, respectively. CAD patients showed the upregulation of IL-6, TNF-α, CDKN2A, CDKN2B, F2RL3, FGF2, P66, and TGFBR3. Hypomethylation of CDKN2A loci was shown to increase risk for CAD by 1.79-folds (95% CI 1.22-2.63). Classification and regression tree (CART) model of gene expression showed increased risk for CAD with F2RL3 > 3.4-fold, while demonstrating risk reduction with F2RL3 < 3.4-fold and IL-6 < 7.7-folds. This CAD prediction model showed the excellent sensitivity (0.98, 95% CI 0.88-1.00), specificity (0.91, 95% CI 0.86-0.92), positive predictive value (0.82, 95% CI 0.75-0.84), and negative predictive value (0.99, 95% CI 0.94-1.00) with an overall accuracy of 92.8% (95% CI 87.0-94.1%). Folate and B12 deficiencies were observed in CAD cases, which were shown to contribute to hypomethylation and upregulation of the prime candidate genes i.e. CDKN2A and F2RL3. Early onset diabetes was associated with IL-6 and TNF-α hypomethylation and upregulation of CDKN2A. The expression of F2RL3 and IL-6 (or) hypomethylation status at CDKN2A locus are potential biomarkers in CAD risk prediction. Early epigenetic imprints of CAD were observed in early onset diabetes. Folate and B12 deficiencies are the contributing factors to these changes in CAD-specific epigenetic signatures.

Newborn screening and single nucleotide variation profiling of TSHR, TPO, TG and DUOX2 candidate genes for congenital hypothyroidism.

High prevalence of congenital hypothyroidism (CH) among Indian newborns prompted us to establish population-specific reference ranges of TSH and to explore the contribution of the common genetic variants in TSHR, TPO, TG and DUOX2 genes towards CH. A total of 1144 newborns (593 males and 551 females) were screened for CH. SNV profiling (n = 22) spanning three candidate genes, i.e. TSHR, TPO and TG was carried out in confirmed CH cases (n = 45). In screen negative cases (n = 700), ten TSHR variants were explored to establish association with CH. No mutation found in DUOX2. The 2.5th to 97.5th percentiles of TSH in these newborns were 0.5 to 12.2 mU/L. In newborns with optimal birth weight, the cut-off was 10 mU/L. Lower or higher birth weight resulted in slightly higher TSH. Two TSHR variants, i.e. rs7144481 and rs17630128 were associated with agenesis, hypoplasia and goiter. The rs2268477 was associated with agenesis and hypoplasia. The rs1991517, rs2075176 and rs2241119 were associated with agenesis only. The rs7144481, rs17630128, rs1991517 and rs2268477 were associated with 2.17, 4.62, 2.91 and 2.29-fold increased risk for CH, respectively. Among the TPO variants, rs867983 and rs2175977 were associated with agenesis and goiter, respectively. Among the TG variants, rs2076740 showed association with agenesis and goiter. Two rare variants i.e. TPO g.IVS14-19 G>C and TG c.1262 C>T were observed in CH cases. No genetic variant identified in the two exons of DUOX2. To conclude, the current study established Indian population-specific normative values for TSH and demonstrates specific genotype-phenotype correlations among three candidate genes.

Artificial neural network and bioavailability of the immunosuppression drug.

PURPOSE OF REVIEW: The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables. RECENT FINDINGS: In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation. SUMMARY: Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.

Adaptive Neuro-Fuzzy Inference System-Based Exploration of the Interrelationships of 25-Hydroxyvitamin D, Calcium, Phosphorus with Parathyroid Hormone Production.

The rationale of the current study was to assess the prevalence of 25-hydroxyvitamin D (25-OHD) deficiency and hyperparathyroidism in South Indian population and to explore interrelationships of 25-OHD, Ca, P towards parathyroid hormone (PTH) production using adaptive neuro-fuzzy inference system (ANFIS). A total of 407 subjects (228 men 179 women) with the mean age 56.8 ± 14.1 were tested for these parameters. In view of the skewed distribution of biochemical variables, data segregation was performed in tertiles and this data was trained to generate fuzzy interference system based on subclusters. The optimized model had 358 nodes and followed 44 fuzzy rules for prediction. This ANFIS model demonstrates that the deficiency of 25-OHD and Calcium triggers PTH production. PTH elevation is significant when Phosphorus is in the highest tertile. The associations observed by this model were consistent with the Kendall-Tau correlation matrix, which revealed inverse associations of Ca with P; and Ca with PTH and positive associations of P with PTH, and Ca with 25-OHD. Furthermore, the association statistics of the machine learning algorithm were also consistent, which suggested that depletion of Ca below 8.245 mg/dl was shown to elevate PTH levels greater than 167 pg/ml when P > 4.66. Subnormal depletion in 25-OHD (9.3-16.2 ng/ml) is associated with subnormal elevation in PTH (47-73.6 pg/ml). To conclude, ANFIS and machine learning algorithm are in agreement with each other in stating that 25-OHD deficiency triggers lower calcium levels, lower calcium and higher phosphorus trigger PTH production.

Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses.

This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used for the qualitative and quantitative analysis of GAGs. Specific enzyme assays and targeted gene sequencing were performed to confirm the diagnosis. Machine learning tools were used to develop CART model based on GAG profile. Qualitative and quantitative CART models showed 96.3% and 98.3% accuracy, respectively, in the differential diagnosis of MPS. The thresholds of different GAGs diagnostic of specific MPS types were established. In 60 MPS positive cases, 46 different mutations were identified in six specific genes. Among 31 different mutations identified in IDUA, nine were nonsense mutations and two were gross deletions while the remaining were missense mutations. In IDS gene, four missense, two frameshift, and one deletion were identified. In NAGLU gene, c.1693C > T and c.1914_1914insT were the most common mutations. Two ARSB, one case each of SGSH and GALNS mutations were observed. LC-MS/MS-based GAG pattern showed higher accuracy in the differential diagnosis of MPS. The mutation spectrum of MPS, specifically in IDUA and IDS genes, is highly heterogeneous among the cases studied.

Microarray-based SNP genotyping to identify genetic risk factors of triple-negative breast cancer (TNBC) in South Indian population.

In the view of aggressive nature of Triple-Negative Breast cancer (TNBC) due to the lack of receptors (ER, PR, HER2) and high incidence of drug resistance associated with it, a case-control association study was conducted to identify the contributing genetic risk factors for Triple-negative breast cancer (TNBC). A total of 30 TNBC patients and 50 age and gender-matched controls of Indian origin were screened for 9,00,000 SNP markers using microarray-based SNP genotyping approach. The initial PLINK association analysis (p < 0.01, MAF 0.14-0.44, OR 10-24) identified 28 non-synonymous SNPs and one stop gain mutation in the exonic region as possible determinants of TNBC risk. All the 29 SNPs were annotated using ANNOVAR. The interactions between these markers were evaluated using Multifactor dimensionality reduction (MDR) analysis. The interactions were in the following order: exm408776 > exm1278309 > rs316389 > rs1651654 > rs635538 > exm1292477. Recursive partitioning analysis (RPA) was performed to construct decision tree useful in predicting TNBC risk. As shown in this analysis, rs1651654 and exm585172 SNPs are found to be determinants of TNBC risk. Artificial neural network model was used to generate the Receiver operating characteristic curves (ROC), which showed high sensitivity and specificity (AUC-0.94) of these markers. To conclude, among the 9,00,000 SNPs tested, CCDC42 exm1292477, ANXA3 exm408776, SASH1 exm585172 are found to be the most significant genetic predicting factors for TNBC. The interactions among exm408776, exm1278309, rs316389, rs1651654, rs635538, exm1292477 SNPs inflate the risk for TNBC further. Targeted analysis of these SNPs and genes alone also will have similar clinical utility in predicting TNBC.

Machine learning algorithm-based risk prediction model of coronary artery disease.

In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction models using ensemble machine learning algorithms (EMLA), multifactor dimensionality reduction (MDR) and recursive partitioning (RP). The EMLA model showed better performance than other models in disease (89.3%) and stenosis prediction (82.5%). This model depicted hypertension and alcohol intake as the key predictors of CAD risk followed by cSHMT C1420T, GCPII C1561T, diabetes, GSTT1, CYP1A1 m2, TYMs 5'-UTR 28 bp tandem repeat and MTRR A66G. MDR and RP models are in agreement in projecting increasing age, hypertension and cSHMTC1420T as the key determinants interacting in modulating CAD risk. Receiver operating characteristic curves exhibited clinical utility of the developed models in the following order: EMLA (C = 0.96) > RP (C = 0.83) > MDR (C = 0.80). The stenosis prediction model showed that xenobiotic pathway genetic variants i.e. CYP1A1 m2 and GSTT1 are the key determinants of percentage of stenosis. Diabetes, diet, alcohol intake, hypertension and MTRR A66G are the other determinants of stenosis. These eleven variables contribute towards 82.5% stenosis. To conclude, the EMLA model exhibited higher predictability both in terms of disease prediction and stenosis prediction. This can be attributed to higher number of iterations in EMLA model that can increase the prediction accuracy.

Whole exome sequencing of breast cancer (TNBC) cases from India: association of MSH6 and BRIP1 variants with TNBC risk and oxidative DNA damage.

Whole exome sequencing in triple negative breast cancer cases (n = 8) and targeted sequencing in healthy controls (n = 48) revealed BRIP1 rs552752779 (MAF: 75% vs. 6.25%, OR 45.00, 95% CI 9.43-243.32), ERBB2 rs527779103 (MAF: 62.5% vs. 7.29%, OR 21.19, 95% CI 5.11-94.32), ERCC2 rs121913016 (MAF: 56.25% vs. 7.29%, OR 16.34, 95% CI 4.02-70.41), MSH6 rs2020912 (MAF: 56.25% vs. 1.04%, OR 122.13, 95% CI 12.29-2985.48) as risk factors for triple negative breast cancer. Construction of classification and regression tree followed by smart pruning identified MSH6 and BRIP1 variants as the major determinants of TNBC (Triple Negative Breast Cancer) risk. Except for ERBB2, all other genes regulate DNA repair and chromosomal integrity. In TNBC cases, two likely pathogenic variations i.e. NCOR1 rs562300336 and PIM1 rs746748226 were observed at frequencies of 18.75% and 12.5%, respectively. Among the 24 variants of unknown significance, MMP9 rs199676062, SYNE1 rs368709678, AURKA rs373550419, ABCC4 rs11568694 have variant allele frequency ≥ 62.5%. These genes regulate metastasis, nuclear modeling, cell cycle and cellular detoxification, respectively. To conclude, aberrations in DNA mismatch repair, nucleotide excision repair or BRCA1 associated genome surveillance mechanism contribute towards triple negative breast cancer.

Comparative analysis of four disease prediction models of Parkinson's disease.

Parkinson's disease (PD) is a multi-factorial disorder with high-penetrant mutations accounting for small percentage of PD. Our previous studies demonstrated individual association of genetic variants in folate, xenobiotic, and dopamine metabolic pathways with PD risk. The rational of the study was to develop a risk prediction model for PD using these genetic polymorphisms along with synuclein (SNCA) polymorphism. We have generated additive, multifactor dimensionality reduction (MDR), recursive partitioning (RP), and artificial neural network (ANN) models using 21 SNPs as inputs and disease outcome as output. The clinical utility of all these models was assessed by plotting receiver operating characteristics curves where in area under the curve (AUC) was used as an index of diagnostic utility of the model. The additive model was the simplest and exhibited an AUC of 0.72. The MDR model showed significant gene-gene interactions between SNCA, DRD4VNTR, and DRD2A polymorphisms. The RP model showed SHMT C1420T as important determinant of PD risk. This variant allele was found to be protective and this protection was nullified by MTRR A66G. Inheritance of SHMT wild allele and SNCA intronic polymorphism was shown to increase the risk of PD. The ANN model showed higher diagnostic utility (AUC = 0.86) compared to all the models and was able to explain 56.6% cases of sporadic PD. To conclude, the ANN model developed using SNPs in folate, xenobiotic, and dopamine pathways along with SNCA has higher clinical utility in predicting PD risk compared to other models.

In silico approaches to identify the potential inhibitors of glutamate carboxypeptidase II (GCPII) for neuroprotection.

To develop a potential inhibitor for glutamate carboxypeptidase II (GCPII) effective against all the eight common genetic variants reported, PyMOL molecular visualization system was used to generate models of variants using the crystal structure of GCPII i.e. 2OOT as a template. High-throughput virtual screening of 29 compounds revealed differential efficacy across the eight genetic variants (pIC50: 4.70 to 10.22). Pharmacophore analysis and quantitative structure-activity relationship (QSAR) studies revealed a urea-based N-acetyl aspartyl glutamate (NAAG) analogue as more potent inhibitor, which was effective across all the genetic variants of GCPII as evidenced by glide scores (-4.32 to -7.08) and protein-ligand interaction plots (13 interactions in wild GCPII). This molecule satisfied Lipinski rule of five and rule of three for drug-likeliness. Being a NAAG-analogue, this molecule might confer neuroprotection by inhibiting glutamatergic neurotransmission mediated by N-acetylated alpha-linked acidic dipeptidase (NAALADase), a splice variant of GCPII.

Multifactor dimensionality reduction analysis to elucidate the cross-talk between one-carbon and xenobiotic metabolic pathways in multi-disease models.

Putatively functional polymorphisms of one-carbon and xenobiotic metabolic pathways influence susceptibility for wide spectrum of diseases. The current study was aimed to explore gene-gene interactions among these two metabolic pathways in four diseases i.e. breast cancer, systemic lupus erythematosus (SLE), coronary artery disease (CAD) and Parkinson's disease (PD). Multifactor dimensionality reduction analysis was carried out on four case-control datasets. Cross-talk was observed between one-carbon and xenobiotic pathways in breast cancer (RFC 80 G>A, COMT H108L and TYMS 5'-UTR 28 bp tandem repeat) and SLE (CYP1A1 m1, MTRR 66 A>G and GSTT1). Gene-gene interactions within one-carbon metabolic pathway were observed in CAD (GCPII 1561 C>T, SHMT 1420 C>T and MTHFR 677 C>T) and PD (cSHMT 1420 C>T, MTRR 66 A>G and RFC1 80 G>A). These interaction models showed good predictability of risk for PD (The area under the receiver operating characteristic curve (C) = 0.83) and SLE (C = 0.73); and moderate predictability of risk for breast cancer (C = 0.64) and CAD (C = 0.63). Cross-talk between one-carbon and xenobiotic pathways was observed in diseases with female preponderance. Gene-gene interactions within one-carbon metabolic pathway were observed in diseases with male preponderance.

Association of estrogen receptor 1 (ESR1) haplotypes with risk for systemic lupus erythematosus among South Indians.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder involving genetic, epigenetic and environmental factors and has higher incidence in women. In this study, we explored the association of estrogen receptor 1 (ESR1) rs2234693 (PvuII) and rs9340799 (XbaI) polymorphisms with susceptibility to SLE. PCR-RFLP and ELISA were used for genetic analysis and determination of specific autoantibodies, respectively. The univariate analysis showed no independent association of rs2234693 (OR: 1.14, 95% CI: 0.87 - 1.49, p = 0.36) and rs9340799 (OR: 0.87, 95% CI: 0.66-1.14, p = 0.34). The haplotype analysis using SHEsis platform revealed strong linkage disequilibrium between these two polymorphisms (D': 0.81, r2: 0.55). Among the four haplotype groups, the C-A haplotype (rs2234693-rs9340799) was strongly associated with the risk for SLE (OR: 2.10, 95% CI: 1.32 - 3.34, p = 0.001). The homozygous variant genotype of rs2234693 exhibited elevated TNF-α and depleted IFN-α, while the effects of rs9340799 were contradictory. The wild genotype of rs2234693 exhibited lower levels of IL-12 with number of rs9340799 variant alleles pronouncing this effect. From this study, it is concluded that the ESR1 haplotypes may influence the Th2 cytokine profile and susceptibility to SLE among the South Indians.

Molecular insights into the association of obesity with breast cancer risk: relevance to xenobiotic metabolism and CpG island methylation of tumor suppressor genes.

Obesity, genetic polymorphisms of xenobiotic metabolic pathway, hypermethylation of tumor suppressor genes, and hypomethylation of proapoptotic genes are known to be independent risk factors for breast cancer. The objective of this study is to evaluate the combined effect of these environmental, genetic, and epigenetic risk factors on the susceptibility to breast cancer. PCR-RFLP and multiplex PCR were used for the genetic analysis of six variants of xenobiotic metabolic pathway. Methylation-specific PCR was used for the epigenetic analysis of four genetic loci. Multifactor dimensionality reduction analysis revealed a significant interaction between the body mass index (BMI) and catechol-O-methyl transferase H108L variant alone or in combination with cytochrome P450 (CYP) 1A1m1 variant. Women with "Luminal A" breast cancer phenotype had higher BMI compared to other phenotypes and healthy controls. There was no association between the BMI and tumor grade. The post-menopausal obese women exhibited lower glutathione levels. BMI showed a positive association with the methylation of extracellular superoxide dismutase (r = 0.21, p < 0.05), Ras-association (RalGDS/AF-6) domain family member 1 (RASSF1A) (r = 0.31, p < 0.001), and breast cancer type 1 susceptibility protein (r = 0.19, p < 0.05); and inverse association with methylation of BNIP3 (r = -0.48, p < 0.0001). To conclude based on these results, obesity increases the breast cancer susceptibility by two possible mechanisms: (i) by interacting with xenobiotic genetic polymorphisms in inducing increased oxidative DNA damage and (ii) by altering the methylome of several tumor suppressor genes.

Adaptive developmental plasticity in methylene tetrahydrofolate reductase (MTHFR) C677T polymorphism limits its frequency in South Indians.

Methylene tetrahydrofolate reductase (MTHFR) C677T polymorphism shows considerable heterogeneity in its distribution in humans worldwide. The current study was conducted to investigate whether this polymorphism exhibited adaptive developmental plasticity in the control of the TT-genotype frequency. We screened 1,818 South Indian subjects (895 males and 923 females) for MTHFR C677T polymorphism using PCR-restriction fragment length polymorphism approach. MTHFR 677T-allele frequency in males and females was 9.1 and 11.0%, respectively. Compared to females, males had lower frequency of TT-genotype [odds ratio 0.31, 95% confidence interval (CI) 0.08-1.01]. The frequency of MTHFR 677T-allele was highest in the age group of 20-40 years and it gradually decreased from 40-60 to 60-80 years (P trend<0.0001). MTHFR 677TT-genotype was associated with 7.02-folds (95% CI: 2.12-25.63, P<0.0001) cumulative risk for recurrent pregnancy loss (RPL), neural tube defects (NTDs) and deep vein thrombosis (DVT). Linear regression model suggested that male gender exhibited increased homocysteine levels by 9.35 µmol/L while each MTHFR 677T-allele contributed to 4.63 µmol/L increase in homocysteine. Plasma homocysteine showed inverse correlation with dietary folate (r=-0.17, P<0.0001), B2 (r=-0.14, P<0.0001) and B6 (r=-0.07, P=0.03). Examination of the spontaneously aborted fetuses (n=35) showed no significant association of fetal genotype on its in utero viability. From the current study, it was concluded that C677T seemed to have acquired adaptive developmental plasticity among South Indians due to environmental influences thus contributing to hyperhomocysteinemia and its associated complications such as RPL, NTDs, DVT, etc.

Sexual dimorphism in xenobiotic genetic variants-mediated risk for Parkinson's disease.

The vulnerability of dopaminergic neurons to environmental exposures in sporadic Parkinson's disease (PD) has been attributed to altered detoxification by xenobiotic metabolizing genes. Hence, we investigated the influence of genetic polymorphisms in xenobiotic metabolic pathway (CYP1A1 m1, CYP1A1 m2, CYP1A1 m4, COMT p.H108L, GSTT1, and GSTM1) on the susceptibility to PD. We used PCR-RFLP for CYP1A1 and COMT genotyping; multiplex-PCR for GSTT1 and GSTM1 deletion analysis; and spectrophotometric methods to evaluate the oxidative stress markers. Results showed association of CYP1A1 m1 (OR: 2.38, 95 % CI: 1.76-3.22) and COMT p.H108L (OR: 2.08 95 % CI: 1.56-2.77) polymorphisms with risk for PD. Male patients carrying combination of COMT p.H108L and CYP1A1 m1 variant alleles showed an early onset of the disease. There was a significant increase in oxidative stress makers such as malondialdehyde and protein carbonyls; and decrease in glutathione levels in PD cases compared to controls (P < 0.05). To conclude, CYP1A1 m1, COMT p.H108L polymorphisms were associated with PD risk, and sexual dimorphism was observed in these associations.