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.

The role of TLR7 agonists in modulating COVID-19 severity in subjects with loss-of-function TLR7 variants.

We investigate the mechanism associated with the severity of COVID-19 in men with TLR7 mutation. Men with loss-of-function (LOF) mutations in TLR7 had severe COVID-19. LOF mutations in TLR7 increased the risk of critical COVID by 16.00-fold (95% confidence interval 2.40-106.73). The deleterious mutations affect the binding of SARS-CoV2 RNA (- 328.66 ± 26.03 vs. - 354.08 ± 27.70, p = 0.03) and MYD88 (ß: 40.279, p = 0.003) to TLR7 resulting in the disruption of TLR7-MyD88-TIRAP complex. In certain hypofunctional variants and all neutral/benign variants, there is no disruption of TLR7-MyD88-TIRAP complex and four TLR7 agonists showed binding affinity comparable to that of wild protein. N-acetylcysteine (NAC) also showed a higher binding affinity for the LOF variants (p = 0.03). To conclude, TLR7 LOF mutations increase the risk of critical COVID-19 due to loss of viral RNA sensing ability and disrupted MyD88 signaling. Majority of hypofunctional and neutral variants of TLR7 are capable of carrying MyD88 signaling by binding to different TLR7 agonists and NAC.

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.

Development of pharmacogenomic algorithm to optimize nateglinide dose for the treatment of type 2 diabetes mellitus.

BACKGROUND: Nateglinide is a meglitinide used for the treatment of type 2 diabetes mellitus. Individual studies demonstrated the association of CYP2C9, SLCO1B1, and MTNR1B variants with the safety and efficacy of nateglinide. The current study aimed to develop a pharmacogenomic algorithm to optimize nateglinide therapy. METHODS: Multiple linear regression (MLR) and classification and regression tree (CART) were used to develop a pharmacogenomic algorithm for nateglinide dosing based on the published nateglinide pharmacokinetic data on the area under the curve data (AUC) and Cmax (n = 143). CYP2C9 metabolizer phenotype, SLCO1B1, MTNR1B genotypes, and CYP2C9 inhibitor usage were used as the input variables. The results and associations were further confirmed by meta-analysis and in silico studies. RESULTS: The MLR models of AUC and Cmax explain 87.4% and 59% variability in nateglinide pharmacokinetics. The Bland and Altman analysis of the nateglinide dose predicted by these two MLR models showed a bias of ± 26.28 mg/meal. The CART algorithm was proposed based on these findings. This model is further justified by the meta-analysis showing increased AUCs in CYP2C9 intermediate metabolizers and SLCOB1 TC and CC genotypes compared to the wild genotypes. The increased AUC in SLCO1B1 mutants is due to decreased binding affinity of nateglinide to the mutant affecting the influx of nateglinide into hepatocytes. MTNR1B rs10830963 G-allele-mediated poor response to nateglinide is attributed to increased transcriptional factor binding causing decreased insulin secretion. CONCLUSION: CYP2C9, SLCO1B1, and MTNR1B genotyping help in optimizing nateglinide therapy based on this algorithm and ensuring safety and efficacy.

Molecular insights into the role of genetic determinants of congenital hypothyroidism.

In our previous studies, we have demonstrated the association of certain variants of the thyroid-stimulating hormone receptor (TSHR), thyroid peroxidase (TPO), and thyroglobulin (TG) genes with congenital hypothyroidism. Herein, we explored the mechanistic basis for this association using different in silico tools. The mRNA 3'-untranslated region (3'-UTR) plays key roles in gene expression at the post-transcriptional level. In TSHR variants (rs2268477, rs7144481, and rs17630128), the binding affinity of microRNAs (miRs) (hsa-miR-154-5p, hsa-miR-376a-2-5p, hsa-miR-3935, hsa-miR-4280, and hsa-miR-6858-3p) to the 3'-UTR is disrupted, affecting post-transcriptional gene regulation. TPO and TG are the two key proteins necessary for the biosynthesis of thyroid hormones in the presence of iodide and H2O2. Reduced stability of these proteins leads to aberrant biosynthesis of thyroid hormones. Compared to the wild-type TPO protein, the p.S398T variant was found to exhibit less stability and significant rearrangements of intra-atomic bonds affecting the stoichiometry and substrate binding (binding energies, ΔG of wild-type vs. mutant: ‒15 vs. ‒13.8 kcal/mol; and dissociation constant, Kd of wild-type vs. mutant: 7.2E-12 vs. 7.0E-11 M). The missense mutations p.G653D and p.R1999W on the TG protein showed altered ΔG (0.24 kcal/mol and 0.79 kcal/mol, respectively). In conclusion, an in silico analysis of TSHR genetic variants in the 3'-UTR showed that they alter the binding affinities of different miRs. The TPO protein structure and mutant protein complex (p.S398T) are less stable, with potentially deleterious effects. A structural and energy analysis showed that TG mutations (p.G653D and p.R1999W) reduce the stability of the TG protein and affect its structure-functional relationship.

Pharmacogenetic determinants of warfarin in the Indian population.

BACKGROUND: Several studies optimized the warfarin dose based on CYP2C9*2, CYP2C9*3, VKORC1 -1639 G > A, CYP4F2 V433M. But, the information on the rare variants is lacking. In this study, we have explored the prevalence of common and rare pharmacogenetic determinants of warfarin and determined their damaging nature. METHODS: We have analyzed 2000 healthy adults using the Infinium global screening array (GSA) for 15 pharmacogenetic determinants of warfarin. In addition, we have elucidated the impact of these variants on protein function, stability, dynamics, evolutionary preservation, and ligand binding propensity. RESULTS: The GSA Analysis has revealed that CYP4F2 V433M (MAF: 39.425%), VKORC1 -1639 G > A (MAF: 20.5%), CYP2C9*3 (MAF:9.925%), and CYP2C9*2 (MAF:4.575%) are common, while CYP2C9*14 (MAF: 1.475%), CYP2C9*4 (0.175%), CYP2C9*5 (0.125%), and CYP2C9*11 (0.125%) are rare. Position-specific evolutionary preservation (PSEP) analysis has revealed that CYP2C9*4 is possibly damaging, while CYP2C9*5, CYP2C9*11, and CYP2C9*14 are probably damaging. CYP2C9*4 has high thermolability (-10.14 kcal/mol). Among the rare CYP2C9 variants, CYP2C9*4 and CYP2C9*11 exert destabilizing effects and may have increased molecular flexibility, while CYP2C9*5 and CYP2C9*14 exert stabilizing effects and may have decreased molecular flexibility. DNase I footprint analysis has revealed the loss of the E-box consensus sequence due to VKORC1 -1639 G > A polymorphism. CONCLUSION: CYP2C9*2, CYP2C9*3, VKORC1 -1639 G > A and CYP4F2 V433M are common; CYP2C9*4, CYP2C9*5, CYP2C9*11, and CYP2C9*14 variants are rare in Indian subjects. All the CYP2C9 variants are found to be damaging. DNase I footprint analysis provided the mechanistic rationale for the association of VKORC1 -1639 G > A with warfarin sensitivity.

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.

Mechanistic insights into the CYP2C19 genetic variants prevalent in the Indian population.

PURPOSE: CYP2C19 metabolizes the antiplatelet and antiepileptic drugs. Any alteration in CYP2C19 activity might influence the therapeutic efficacy. The objective of this study was to identify CYP2C19 variants prevalent in Indians and perform their in silico characterization. METHODS: Infinium global screening array (GSA) was used for CYP2C19 genotyping in 2000 healthy Indians. In addition, we performed in silico characterization of the identified variants. RESULTS: Out of the 11 variants covered (*2, *3, *4,*5,*6, *7,*8, *9,*10,*11, and *17), five were identified in Indians (*2, *3, *6,*8 and *17). The *2 and *17 were the most prevalent alleles (minor allele frequencies, MAF: 32.0% and 13.95%). The *3, *6 and *8 were rare (MAFs: 0.425%, 0.025% and 0.05%). The *2 variant is shown to affect the splicing at the fifth exon-intron boundary. The *3 variant is a non-sense variant that is predicted to be deleterious. On the otherhand, the *17 variant showed more binding affinity for GATA binding protein 1 (GATA1), myocyte enhancer factor 2 (MEF2) and ectotropic viral integration site 1 (EVI1). The *6 and *8 variants predicted to be deleterious. The *2, *3 and *7 variants showed lesser probability of exon skipping, while *17 showed more probability. The genotype distribution of Indian subjects is comparable with that of South Asians (SAS) (1000 genome project, phase 3). CONCLUSION: The *2, *3 and *17 variants are the key pharmacogenetic determinants in Indians. The *2 and *3 are loss-of-function variants. The *17 is a gain-of-function variant with increased binding of transcriptional factors.

Alpha synuclein (SNCA) rs7684318 variant contributes to Parkinson's disease risk by altering transcription factor binding related with Notch and Wnt signaling.

In view of inconsistencies in the association studies of alpha synuclein (SNCA) rs7684318 (chr4: 90655003 T > C) with Parkinson's disease (PD), we conducted a meta-analysis to establish the association of this variant with PD and examined changes in transcription factor binding. SNCA rs7684318 C-allele was identified as genetic risk factor for PD in fixed (OR: 1.53, 95 % CI: 1.40-1.68, p < 0.0001) and random effect (OR: 1.65, 95 % CI: 1.30-2.09, p = 0.0003) models. Heterogeneity was observed in association (Tau2: 0.0576, H: 2.32, I2: 0.815, Q: 21.64, p = 0.0002). Egger's test showed no evidence of publication bias (p = 0.37). Subgroup analysis showed that rs7684318 is contributing to PD risk in Japanese (OR: 1.46, 95 % CI: 1.30-1.64) and Indian (OR: 2.63, 95 % CI: 1.79-3.86) populations while showing no significant association in Chinese population (OR: 1.68, 95 % CI: 0.93-3.02). Sensitivity analysis showed that exclusion of any one of the studies has no significant impact on the association, which justifies the robustness of the analysis. Tissue-specific DNase foot print analysis revealed that this variant contributes to increased transcription factor binding in midbrain, putamen and caudate nucleus. The substitution of T > C increased binding of RBPJ and GATA-family transcription factors; and decreased binding of NKX2 family, SNAI2, SNAI3, DMRT1, HOXA13, HOXB13, HOXC13, HOXD13, WT1, POU4F1, POU4F2, POU4F3 transcriptional factors. TRANSFAC and DNA curvature analyses substantiate the association of this variant with increased binding of GATA1 that contribute to intensity of DNA curvature peaks and splitting pattern. These studies along with the meta-analysis strongly suggest that the rs7684318 variant contributes to the pathophysiology of PD by modulating binding of transcription factors related to Notch and Wnt signalling pathways that are likely to impair dopmanergic transmission.

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.

Pharmacogenetic determinants of thiopurines in an Indian cohort.

BACKGROUND: Several genetic variants of thiopurine metabolic pathway are associated with 6-thiopurine-mediated leucopenia. A population-based evaluation of these variants lays the foundation for Pharmacogenetic-guided thiopurine therapy. METHODS: A total of 2000 subjects were screened for the pharmacogenetic determinants using the infinium global screening array (GSA). The functional relevance of these variants was deduced using SNAP2, SIFT, Provean, Mutalyzer, Mutation Taster, Phyre2, SwissDock, AGGRESCAN, and CUPSAT. RESULTS: The minor allele frequencies of NUDT15*3, NUDT15*5, TPMT*3C, TPMT*3B variant alleles were 6.78%, 0.11%, 1.98% and 0.69%, respectively. TPMT*3A genotype was observed in 0.35% subjects. No gender-based differences were observed in the incidence of these variants. Data from studies of the Indian population showed that 92.86% subjects heterozygous for NUDT15*3 and 60% subjects heterozygous for TPMT*3C exhibit thiopurine-mediated hematological toxicity. NUDT15 variants have no impact on the binding of 'dGTP' to the NUDT protein. NUDT15*3 variant increases aggregation 'hot spot' region and induces unfavourable torsion in the protein. NUDT15*5 destabilizes the protein and impairs Mg/Mn binding. TPMT*3A, TPMT*3B and TPMT*3C variants lower binding affinity to 6-mercaptopurine compared to the wild protein. TPMT*3C variant destabilizes the TPMT protein in the thermal experiment. Compared to the data of European and African/African American populations, NUDT15*3 frequency is higher and TPMT*3C frequency is lower in our population. CONCLUSIONS: TPMT variants were less frequent in Indian population, while NUDT15*3 is more frequent compared to European and African/African American populations. NUDT15*3 increases aggregation 'hot spot' and induces unfavourable torsion in the protein. NUDT15*5 and TPMT*3C destabilize the respective proteins. TPMT*3A, TPMT*3B and TPMT*3C are associated with a lower binding affinity towards 6-mercaptopurine.

Evaluation of Galectin-3 as a Novel Diagnostic Biomarker in Patients with Heart Failure with Preserved Ejection Fraction.

Background Heart failure is a complex cardiovascular disease with a variety of etiologies and heterogeneity. The N-terminal pro-B-type natriuretic peptide (NT-proBNP) value has limited usefulness in diagnosing heart failure with preserved ejection fraction (HFpEF). Aim The aim of the present study is to evaluate serum Galectin-3 as a diagnostic biomarker in patients with HFpEF and to compare Galectin-3 with NT-proBNP levels. Materials and Methods A cross-sectional case-control study including 63 cases of heart failure with ejection fraction ≥50% confirmed by echocardiography. NT-proBNP levels in serum were measured by electrochemiluminescence immunoassay and Galectin-3 levels in serum were measured by using an enzyme-linked-immunosorbent serologic assay kit. Results The median levels of serum Galectin-3 and NT-proBNP in patients were significantly higher than those of controls (26.59 vs. 5.27 and 927 vs. 49.3, p < 0.0001). A positive correlation was observed between serum levels of Galection-3 and NT-ProBNP ( r : 0.21, p = 0.048). At cut-off values of 10.1 ng/mL and 160 pg/mL, serum Galectin-3 has 77.78% sensitivity, 95% specificity with an area under the curve (AUC) of 0.93, and serum NT-proBNP has 71.43% sensitivity, 100% specificity with an AUC of 0.87, respectively, for diagnosing HFpEF. The comparison of receiver operating characteristics curves showed that Galectin-3 has better AUC compared with NT-proBNP in diagnosing HFpEF. Serum Galectin-3 showed a positive correlation with NT-proBNP and lipid parameters. Conclusion Galectin-3 with higher sensitivity and AUC can be used as a valuable biomarker for the diagnosis of HFpEF. Simultaneous testing of both Galectin-3 and NT-proBNP can further improve the detection of patients with HFpEF.

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.

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.

The rs1991517 polymorphism is a genetic risk factor for congenital hypothyroidism.

The objective of the current study is to explore the association of thyroid-stimulating hormone receptor (TSHR) rs1991517 polymorphism (c.2337 C > G, p.D727E) with congenital hypothyroidism (CH) through a case-control study followed by a meta-analysis. The case-control study was based on 45 CH subjects and 700 healthy controls. Meta-analysis comprised of seven published studies and our current findings (1044 CH cases and 1649 healthy controls). The allele contrast model showed that the presence of G- allele increased CH risk by 45% (OR: 1.45, 95% CI 1.20-1.76) and 41% (OR: 1.41, 95% CI 1.03-1.93) in fixed effect and random effect models, respectively. The GG- genotype is associated with 2.3-fold (95% CI 1.32-3.99) increased risk for CH in the fixed-effect model. I 2 (0.58) and Cochran's Q test (Q: 16.72, p = 0.02) revealed evidence of heterogeneity in the association. No publication bias was observed by Egger's test (p = 0.70). Sensitivity analysis revealed that even after excluding any study this polymorphism is associated with risk for CH. The rs1991517 mutation alters the binding affinity to cAMP (ΔG of 727D vs.727E: - 7.27 vs. - 7.34 kcal/mol). In conclusion, rs1991517 is a genetic risk factor for CH and exerts its impact by altering cAMP-mediated signal transduction.

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.

Clinical and Molecular Diagnosis of Joubert Syndrome and Related Disorders.

BACKGROUND: Joubert syndrome and related disorders are a group of ciliopathies characterized by mid-hindbrain malformation, developmental delay, hypotonia, oculomotor apraxia, and breathing abnormalities. Molar tooth sign in brain imaging is the hallmark for diagnosis. Joubert syndrome is a clinically and genetically heterogeneous disorder involving mutations in 35 ciliopathy-related genes. We present a large cohort of 59 patients with Joubert syndrome from 55 families. Molecular analysis was performed in 35 families (trio). METHODS: Clinical exome analysis was performed to identify causal mutations, and genotype-phenotype correlations were evaluated. RESULTS: All of the cases were stratified into pure Joubert syndrome (62.7%), Joubert syndrome with retinal disease (22.0%), polydactyly (8.5%), and liver (1.7%) and kidney (1.7%) involvement. Joubert syndrome-related disorders include Meckel-Gruber syndrome in 5.1% cases and Leber congenital amaurosis (1.7%). Of the 35 Joubert syndrome-related genes, 11 were identified in these patients, i.e., CEP290, C5ORF, TCTN1, CC2D2A, RPGRP1L, TCTN3, AHI1, INPP5E, TCTN2, NPHP1, and TMEM237. For the first time, we identified a ciliopathy gene, CCDC28B, as a causal gene in Joubert syndrome in one family. CEP290 accounted for 37.8% cases of pure Joubert syndrome, Joubert syndrome with retinal and renal disease, and Meckel-Gruber syndrome. The p.G1890∗ allele in CEP290 is highly recurrent. Of the six families with Joubert syndrome who had a prenatal diagnosis, one fetus was normal, two were carriers, and three were affected. CONCLUSIONS: This is the largest study of Joubert syndrome from India. Although a high degree of locus and allelic heterogeneity was observed, CEP290 variants were the most common among these patients.

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.