Tissue-specific DNase I footprint analysis confirms the association of GATAD2B Q470* variant with intellectual disability.

Intellectual disability (ID) is a neurodevelopmental disorder in which genetics play a key aetiological role. GATA zinc finger domain-containing 2B (GATAD2B) gene encodes a zinc-finger protein transcriptional repressor which is a part of the methyl-CpG binding protein-1 complex. Pathogenic variants in this gene are linked to ID, dysmorphic features, and cognitive disability. To date, only 18 cases are reported worldwide and only one case is reported from India. A 12-year-old girl presented with a heterozygous nonsense variation in exon 8 of the GATAD2B gene (chr1:153785737G>A). She has severe ID and significant delayed developmental milestones along with clinical features including broad arched eyebrows, low-set ears, a bulbous nose tip, thin upper lip, and wide mouth with downturned corners. This is the second report of a heterozygous mutation in the GATAD2B gene from India with a novel phenotype. To substantiate the association of GATAD2B mutation with ID, we performed DNase I footprint analysis of wild and mutant DNA sequences to establish k-mer binding profile and deduced GATA binding affinity using human ENCODE experimental data of foetal brain. We observed that in the presence of variation, GATA zinc finger domain was altered thus contributing to ID. Our findings support the importance of the GATAD2B gene in the study of neurodevelopmental disorders.

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.

A pilot study on machine learning approach to delineate metabolic signatures in intellectual disability.

Intellectual disability (ID) is a neurodevelopmental disorder characterized by cognitive delays. Inborn errors of metabolism constitute an important subgroup of ID for which various treatments options are available. We aimed to identify potential biomarkers of inherited metabolic disorders from the children with ID using tandem mass spectrometry and develop a novel machine learning algorithm to differentiate between the cases and the controls. All of the cases were having IQ score <70, gross motor delay, speech disorder and no recognizable symptoms of the condition. Metabolite profiling of ID individuals exhibited low tyrosine/large neutral amino acids, high citrulline/arginine ratios; elevated proline, alanine, phenylalanine, and ornithine, while a significant decrease in the level of amino acid arginine, and elevated C4 (butyrylcarnitine) and C4OH/C3DC (3-hydroxybutyrylcarnitine/malonylcarnitine). Machine learning algorithm differentiated cases and controls efficiently using specific thresholds of ornithine, arginine and C4OH/C3DC. Furthermore, ID cases were distinguished into mild, moderate, and severe based on specific thresholds of methionine, arginine, and C5OH/C4DC (3-hydroxyisovalerylcarnitine/methylmalonylcarnitine). The machine learning algorithm could successfully identify specific metabolite markers in ID and correlate the same with neurological features.

Clinical utility of folate pathway genetic polymorphisms in the diagnosis of autism spectrum disorders.

BACKGROUND: The rationale of the current study was to test the clinical utility of the folate pathway genetic polymorphisms in predicting the risk for autism spectrum disorders (ASD) and to address the inconsistencies in the association of MTHFR C677T and hyperhomocysteinemia with ASD. PATIENTS AND METHODS: An artificial neural network (ANN) model was developed from the data of 138 autistic and 138 nonautistic children using GCPII C1561T, SHMT1 C1420T, MTHFR C677T, MTR A2756G, and MTRR A66G as the predictors of autism risk. A neuro fuzzy model was developed to explore the genetic determinants of homocysteine. Meta-analyses were carried out on 1361 ASD children and 6591 nonautistic children to explore the association of MTHFR C677T and homocysteine with the risk for ASD. RESULTS: The ANN model showed 63.8% accuracy in predicting the risk of autism. Hyperhomocysteinemia was observed in autistic children (9.67±4.82 vs. 6.99±3.21 µmol/l). The neuro fuzzy model showed synergistic interactions between MTHFR C677T and MTRR A66G inflating homocysteine levels. The meta-analysis showed MTHFR to be a genetic risk factor for autism in both fixed-effects (odds ratio: 1.47, 95% confidence interval: 1.31-1.65) and random-effects (odds ratio: 1.57, 95% confidence interval: 1.16-2.11) models. The meta-analysis of nine studies showed hyperhomocysteinemia as a significant risk factor for autism in both fixed-effects (P<0.0001) and random-effects (P=0.026) models. CONCLUSION: Genetic polymorphisms of the folate pathway were moderate predictors of autism risk. MTHFR C677T and hyperhomocysteinemia have been identified as risk factors for autism worldwide. Synergistic interactions between MTHFR C677T and MTRR A66G increase homocysteine.

Genetic and environmental influences on total plasma homocysteine and coronary artery disease (CAD) risk among South Indians.

BACKGROUND: Hyperhomocysteinemia, a documented risk factor for CAD is highly prevalent in Indians. The rationale behind the current study is to explore the genetic and environmental causes for such high prevalence as there are limited studies in this context. METHODS: A total of 108 CAD cases and 108 controls were analyzed for tHcy and 4 folate pathway genetic polymorphisms [methylene tetrahydrofolate reductase (MTHFR) C677T, 5-methyltetrahydrofolate homocysteine methyl transferase (MTR) A2756G, methionine synthase reductase (MTRR) A66G and glutamate carboxypeptidase II (GCPII) C1561T] using reverse phase HPLC and PCR-RFLP methods respectively. RESULTS: MTHFR 677T, MTRR 66A, GCPII 1561T, male gender, alcohol intake, smoking, diabetes, creatinine and hypertension were found to influence tHcy. After controlling for confounding factors, Hyperhomocysteinemia and two of its genetic determinants i.e. MTHFR C677T [OR: 1.96, 95% CI: 1.06-3.61] and GCP II C1561T [OR: 2.09, 95% CI: 1.09-3.97] were found to be associated with risk for CAD. Significant epistatic interactions were observed between MTHFR 677T/MTR 2756G and GCP II 1561T/MTRR 66G. Alcohol intake in subjects with MTR 2756G allele was found to inflate the risk for CAD [OR: 4.15, 95% CI: 1.35-12.69]. CONCLUSION: Hyperhomocysteinemia, C677T MTHFR and C1561T GCPII are risk factors for CAD. Potential gene--gene and gene--environment interactions indicate the need for multi-variate analyses for risk prediction.