Biology subjects

Bedrosian, T. A.

Publications and source records attributed to Bedrosian, T. A..

3 recordsLinked to original sources

bioRxiv2026

Disrupted O-GalNAc glycosylation as a mechanism and biomarker of SLC35A2-associated epilepsy

Rare germline and somatic variants in SLC35A2 cause a spectrum of severe glycosylation disorders that commonly present with epilepsy. SLC35A2 encodes the Golgi transporter for UDP-galactose, but how its deficiency leads to severe neurodevelopmental disorders is unknown. Using a mouse model deficient for Slc35a2 in the forebrain, we identified a specific defect in O-GalNAc glycan synthesis, while other galactose-containing glycoconjugates remained intact. O-GalNAc glycans were absent from their normal location within neuronal tracts of the corpus callosum, and truncated precursors accumulated in the cortex on critical extracellular matrix molecules. Cultured primary neurons lacking Slc35a2 showed impaired development, hyperexcitability, and impaired O-GalNAc glycosylation. Finally, human brain tissue from cases of SLC35A2-associated intractable epilepsy displayed a strong correlation between variant burden and truncated O-GalNAc glycans. These findings provide a mechanistic link between genetic causes of SLC35A2-associated epilepsy and protein O-glycosylation that can be targeted for biomarker and therapeutic development.

neuroscience↗

bioRxiv2023

Loss of Slc35a2 alters development of the mouse cerebral cortex

Brain somatic variants in SLC35A2 are associated with clinically drug-resistant epilepsy and developmental brain malformations, including mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE). SLC35A2 encodes a uridine diphosphate galactose translocator that is essential for protein glycosylation; however, the neurodevelopmental mechanisms by which SLC35A2 disruption leads to clinical and histopathological features remain unspecified. We hypothesized that focal knockout (KO) or knockdown (KD) of Slc35a2 in the developing mouse cortex would disrupt cerebral cortical development through altered neuronal migration and cause changes in network excitability. We used in utero electroporation (IUE) to introduce CRISPR/Cas9 and targeted guide RNAs or short-hairpin RNAs to achieve Slc35a2 KO or KD, respectively, during early corticogenesis. Following Slc35a2 KO or KD, we observed disrupted radial migration of transfected neurons evidenced by heterotopic cells located in lower cortical layers and in the sub-cortical white matter. Slc35a2 KO in neurons did not induce changes in oligodendrocyte number, suggesting that the oligodendroglial hyperplasia observed in MOGHE originates from distinct cell autonomous effects. Spontaneous seizures were not observed, but intracranial EEG recordings after focal KO showed a reduced seizure threshold following pentylenetetrazol injection. These results demonstrate that Slc35a2 KO or KD in vivo disrupts corticogenesis through altered neuronal migration.

neuroscience↗

bioRxiv2021

Discovery of Clinically Relevant Fusions in Pediatric Cancer

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. ResultsOur ensemble fusion detection approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and AWS serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the ensemble fusion-calling pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patients medical record, both known and novel fusions provided medically meaningful information. ConclusionsOur ensemble fusion detection pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

genomics↗