Biology subjects

Bayrak-Toydemir, P.

Publications and source records attributed to Bayrak-Toydemir, P..

3 recordsLinked to original sources

bioRxiv2026

ARID5B mutations cause a neurodevelopmental syndrome with neuroinflammation episodes

Genetic disorders affecting the epigenetic machinery constitute a major group of neurodevelopmental conditions. Pathogenic variants in several ARID transcription factors--particularly ARID1A, ARID1B, and ARID2--cause Coffin-Siris syndromes, all characterized by intellectual disability (ID). These genes encode core subunits of the BRG1/BRM-associated factor (BAF) chromatin remodeling complex. In contrast, ARID family members that function in other regulatory complexes have remained largely unexplored in neurodevelopmental disease. Here, we identify 29 individuals carrying heterozygous ARID5B variants, of which 24 (83%) introduce premature termination codons in the exceptionally long final exon, one affects the exon 9 splice donor site, and four are missense variants in conserved domains within the N-terminal half of the protein. Using a CRISPR-Cas9 knock-in mouse model harboring the p.Q522Ter variant, together with in vitro assays, we investigated the functional consequences of C-terminal ARID5B truncations. All affected individuals presented with global developmental delay or ID--most commonly mild--and frequent speech and language impairment. Recurrent features included kidney malformations, behavioral difficulties, and recurrent infections of the respiratory and urinary tracts. Two individuals experienced central nervous system inflammation, and two infants presented with persistent pulmonary hypertension. Remarkably, 19 of 29 variants (66%) cluster within the first quarter of exon 10, are de novo, and escape nonsense-mediated mRNA decay (NMD), which we confirmed for two variants affecting seven individuals. Variants outside this region were inherited. Heterozygous mice exhibited developmental and behavioral abnormalities, while homozygous mutations was perinatally lethal. Truncations and a small deletion within a predicted nuclear localization signal (NLS) caused cytosolic mislocalization of ARID5B, whereas the isolated C-terminal half retained nuclear localization, suggesting an independent distal NLS. Collectively, these findings define ARID5B-related neurodevelopmental disorder as a distinct clinical entity and reveal how disruption of specific ARID5B domains impacts protein localization, mammalian development, immune and neurobehavioral function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/698931v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@158e8d9org.highwire.dtl.DTLVardef@1f014dorg.highwire.dtl.DTLVardef@18e6e7forg.highwire.dtl.DTLVardef@1f886e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2022

Combining genetic constraint with predictions of alternative splicing to prioritize deleterious splicing in rare disease studies

BackgroundDespite numerous molecular and computational advances, roughly half of patients with a rare disease remain undiagnosed after exome or genome sequencing. A particularly challenging barrier to diagnosis is identifying variants that cause deleterious alternative splicing at intronic or exonic loci outside of canonical donor or acceptor splice sites. ResultsSeveral existing tools predict the likelihood that a genetic variant causes alternative splicing. We sought to extend such methods by developing a new metric that aids in discerning whether a genetic variant leads to deleterious alternative splicing. Our metric combines genetic variation in the Genome Aggregate Database with alternative splicing predictions from SpliceAI to compare observed and expected levels of splice-altering genetic variation. We infer genic regions with significantly less splice-altering variation than expected to be constrained. The resulting model of regional splicing constraint captures differential splicing constraint across gene and exon categories, and the most constrained genic regions are enriched for pathogenic splice-altering variants. Building from this model, we developed ConSpliceML. This ensemble machine learning approach combines regional splicing constraint with multiple per-nucleotide alternative splicing scores to guide the prediction of deleterious splicing variants in protein-coding genes. ConSpliceML more accurately distinguishes deleterious and benign splicing variants than state-of-the-art splicing prediction methods, especially in "cryptic" splicing regions beyond canonical donor or acceptor splice sites. ConclusionIntegrating a model of genetic constraint with annotations from existing alternative splicing tools allows ConSpliceML to prioritize potentially deleterious splice-altering variants in studies of rare human diseases.

genomics↗

bioRxiv2020

Effective variant filtering and expected candidate variant yield in studies of rare human disease

In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we derive effective guidelines for variant filtering and report the expected number of candidates for de novo dominant and recessive modes of inheritance. The filters are applied to common attributes, including genotype quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield approximately 10 candidate SNP and INDEL variants per exome, and 19 per genome. For whole genomes, this includes an average of three de novo, ten compound-heterozygotes, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

bioinformatics↗