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Biology subjects

Advani, D.

Publications and source records attributed to Advani, D..

3 recordsLinked to original sources

Phenotype Dependent Segregation of a Novel EPS8 Variant for Hearing Loss and an HPDL Variant for Neurodevelopmental Disorders in a Complex Consanguineous Family

Consanguinity increases the risk of autosomal recessive disorders and may result in the co-segregation of multiple pathogenic variants within the same family. Although most affected families are explained by a single genetic diagnosis, multilocus pathogenic variation can produce complex and overlapping clinical phenotypes. We investigated a consanguineous Pakistani family with three affected siblings, including dizygotic twins presenting with neurodevelopmental disorder and hearing loss, using detailed clinical evaluation, long-read whole-genome sequencing, bulk transcriptomics, protein profiling, and segregation analysis to determine the underlying molecular diagnoses. One sibling presented with isolated non-syndromic hearing loss, whereas the dizygotic twins exhibited severe neurodevelopmental impairment characterized by global developmental delay, spastic quadriplegic cerebral palsy, microcephaly, and white matter abnormalities. Long-read whole-genome sequencing identified a homozygous start-loss variant in HPDL (c.3G>C) in both twins, consistent with HPDL-related neurodevelopmental disorder with progressive spasticity and brain white matter abnormalities (NEDSWMA). In addition, a novel homozygous nonsense variant in EPS8 (c.1294C>T) was identified in one twin and the sibling with isolated hearing loss, explaining the auditory phenotype. Long-read transcriptomic analysis demonstrated absence of detectable EPS8 transcripts in both individuals homozygous for the nonsense variant, providing transcript-level evidence consistent with a loss-of-function mechanism. Genome-wide comprehensive proteomic profiling (SomaScan) identified distinct protein abundance profiles across family members, with the most pronounced alterations observed in the twins affected by HPDL-related neurodevelopmental disease, particularly the individual harboring pathogenic variants in both EPS8 and HPDL. This study expands the mutational spectrum of EPS8 and highlights the independent segregation of two autosomal recessive disorders within the complex consanguineous family, resulting in distinct and blended phenotypes.

genetics↗

Isoform-Resolved Genetic Architecture of Epilepsy and SUDEP Reveals Divergent Brain and Heart Channelopathy Signatures

Sudden unexpected death in epilepsy (SUDEP) is the most devastating complication of epilepsy, yet the molecular features distinguishing individuals at risk remain poorly defined. Although epilepsy and SUDEP share substantial genetic overlap, fatal outcomes may arise when shared risk genes are differentially deployed across neuronal and cardiac systems. Here, we identify tissue- and isoform-level regulation as a key determinant of divergence between epilepsy and SUDEP risk. We performed a large-scale integrated analysis of genetic variants reported in epilepsy and SUDEP across 419 sequencing-based studies encompassing 35,659 individuals, and quantified gene-level burden using a Bayesian Poisson-Gamma rate ratio framework. This analysis revealed preferential enrichment of genes related to cardiac electrophysiology and contractile function in SUDEP, whereas epilepsy was dominated by genes involved in neuronal excitability and synaptic signaling. To determine how shared genetic loci are deployed across tissues, we integrated GTEx-based tissue expression profiles with long-read single-cell transcriptomic datasets from human heart and brain to resolve isoform-level expression patterns. These analyses revealed pronounced tissue-specific transcript architectures. Cardiac-associated genes, including HCN4, KCNH2, KCNE1, MYH6, MYO18B, and ATP1A2, showed heart-restricted isoform expression, whereas neuronal genes such as ADGRV1, CACNA1A, GRIN2B, HCN1, HCN2, KCNA1, SCN1A, SCN2A, and SCN8A. Importantly, several shared genes exhibited tissue-partitioned isoform expression, with distinct transcript repertoires in heart and brain, particularly across pathways related to ion transport, signaling, metabolism, and structural organization. Consistent patterns were observed in iPSC-derived cardiomyocytes and neurons, indicating that lineage-dependent deployment of shared genes is preserved in controlled systems. Together, these findings suggest that tissue-specific isoform regulation provides a mechanistic basis linking shared epilepsy genetics to SUDEP susceptibility, whereby the same genetic loci contribute to neuronal dysfunction in epilepsy and to cardiac vulnerability in SUDEP. This positions SUDEP as a neuro-cardiac interface disorder shaped by isoform-level regulatory divergence.

genetics↗

PanScan: A Tool for Tertiary Analysis of Human Pangenome Graphs

The genomic representation of populations across the globe is critical to ensuring a comprehensive and equitable human reference. Constructing a pangenome graph reference for different populations is the best approach to addressing local genomic diversities. Although major initiatives across continents are underway to construct pangenome graph references, the field lacks the necessary toolsets for tertiary analysis to characterize telomere-to-telomere (T2T) assemblies and the complexity of haplotypes. PanScan is a bioinformatics software package developed for human pangenome tertiary analysis. It includes multiple modules designed to detect duplicated gene sets from T2T assemblies, identify novel variants and sequences, as well as detect and visualize complex genomic regions through pangenome graph haplotype loops. We have used multiple pangenomes across different populations to assess the tertiary analysis and their accuracy. The tool is designed to streamline tertiary analysis and is compatible with multiple pangenome graph construction algorithms. PanScan is freely available on GitHub (https://github.com/CATG-Github/panscan), where users can provide human pangenome assemblies or VCF files as inputs for automated analyses through command-line operations on Linux systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/651685v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@16cc212org.highwire.dtl.DTLVardef@13961d0org.highwire.dtl.DTLVardef@44d194org.highwire.dtl.DTLVardef@1b72aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗