Neuroimaging to Genotype: Delineating the Spectrum of Disorders With Deficient Myelination in the Indian Population.

Several genetic disorders are associated with either a permanent deficit or a delay in central nervous system myelination. We investigated 24 unrelated families (25 individuals) with deficient myelination after clinical and radiological evaluation. A combinatorial approach of targeting and/or genomic testing was employed. Molecular diagnosis was achieved in 22 out of 24 families (92%). Four families (4/9, 44%) were diagnosed with targeted testing and 18 families (18/23, 78%) were diagnosed using broad genomic testing. Overall, 14 monogenic disorders were identified. Twenty disease-causing variants were identified in 14 genes including PLP1, GJC2, POLR1C, TUBB4A, UFM1, NKX6-2, DEGS1, RNASEH2C, HEXA, ATP7A, SETBP1, GRIN2B, OCLN, and ZBTB18. Among these, nine (45%) variants are novel. Fourteen families (82%, 14/17) were diagnosed using proband-only exome sequencing (ES) complemented with deep phenotyping, thus highlighting the utility of singleton ES as a valuable diagnostic tool for identifying these disorders in resource-limited settings.

Genetic and phenotypic landscape of pediatric-onset epilepsy in 142 Indian families: Counseling and therapeutic implications.

The application of genomic technologies has led to unraveling of the complex genetic landscape of disorders of epilepsy, gaining insights into their underlying disease mechanisms, aiding precision medicine, and providing informed genetic counseling. We herein present the phenotypic and genotypic insights from 142 Indian families with epilepsy with or without comorbidities. Based on the electroclinical findings, epilepsy syndrome diagnosis could be made in 44% (63/142) of the families adopting the latest proposal for the classification by the ILAE task force (2022). Of these, 95% (60/63) of the families exhibited syndromes with developmental epileptic encephalopathy or progressive neurological deterioration. A definitive molecular diagnosis was achieved in 74 of 142 (52%) families. Infantile-onset epilepsy was noted in 81% of these families (61/74). Fifty-five monogenic, four chromosomal, and one imprinting disorder were identified in 74 families. The genetic variants included 65 (96%) single-nucleotide variants/small insertion-deletions, 1 (2%) copy-number variant, and 1 (2%) triplet-repeat expansion in 53 epilepsy-associated genes causing monogenic disorders. Of these, 35 (52%) variants were novel. Therapeutic implications were noted in 51% of families (38/74) with definitive diagnosis. Forty-one out of 66 families with monogenic disorders exhibited autosomal recessive and inherited autosomal dominant disorders with high risk of recurrence.

Drug-food Interactions in the Era of Molecular Big Data, Machine Intelligence, and Personalized Health.

The drug-food interaction brings forth changes in the clinical effects of drugs. While favourable interactions bring positive clinical outcomes, unfavourable interactions may lead to toxicity. This article reviews the impact of food intake on drug-food interactions, the clinical effects of drugs, and the effect of drug-food in correlation with diet and precision medicine. Emerging areas in drug-food interactions are the food-genome interface (nutrigenomics) and nutrigenetics. Understanding the molecular basis of food ingredients, including genomic sequencing and pharmacological implications of food molecules, helps to reduce the impact of drug-food interactions. Various strategies are being leveraged to alleviate drug-food interactions; measures including patient engagement, digital health, approaches involving machine intelligence, and big data are a few of them. Furthermore, delineating the molecular communications across dietmicrobiome- drug-food-drug interactions in a pharmacomicrobiome framework may also play a vital role in personalized nutrition. Determining nutrient-gene interactions aids in making nutrition deeply personalized and helps mitigate unwanted drug-food interactions, chronic diseases, and adverse events from their onset. Translational bioinformatics approaches could play an essential role in the next generation of drug-food interaction research. In this landscape review, we discuss important tools, databases, and approaches along with key challenges and opportunities in drug-food interaction and its immediate impact on precision medicine.