Biology subjects

Thrasher, A.

Publications and source records attributed to Thrasher, A..

5 recordsLinked to original sources

bioRxiv2025

A modified cyclosporine enhances lentivector transduction ex vivo and in vivo by degrading IFITM3

Intrinsic innate immune barriers have evolved to suppress viral infection and can reduce effective gene delivery in gene therapy. We have developed BG147, a novel cyclosporine A analogue, optimised via structure-guided design to prevent inhibition of HIV cofactor Cyclophilin A and to specifically inhibit interferon-induced transmembrane proteins (IFITM1-3). BG147 enhances VSV-G pseudotyped lentiviral vector transduction ex vivo in hematopoietic stem and progenitor cells (HSPCs) and in in vivo ocular gene therapy of photoreceptor cells in mice. Upon BG147 treatment, IFITM proteins are mislocalised and degraded through lysosomal acidification-dependent pathways. IFITM3 levels functionally return in cells 96 h after BG147 washout. BG147 promises to transform ex vivo and in vivo eye gene therapies by transiently inhibiting intrinsic immune barriers mediated by IFITM proteins to enhance a wide range of protocols. One Sentence SummaryModified cyclosporine, BG147, enhances lentivector gene therapy transduction, ex vivo in HSPC and in vivo in mouse photoreceptors, by degrading IFITM3.

cell biology↗

bioRxiv2025

Confounding factors in assessing the enriched expression of somatic mutant allele in bulk tumor samples

Allele-specific expression (ASE) of somatic mutations can be caused by cis-activation of the mutant allele or silencing of the wildtype allele and has been investigated by examining the enrichment of mutant allele in RNA relative to DNA. Here we show that this mutation-based approach can be confounded by gene expression differences in tumor and normal cells which co-exist in most bulk tumor samples. We modeled mutant allele expression by incorporating tumor/normal expression difference, mutant allele dosage, tumor purity, and nonsense-mediated decay (NMD) efficiency, projecting that the enrichment can occur without ASE. This confounding effect is exacerbated with low tumor purity and is dependent on mutant allele dosage for NMD-sensitive mutations. The model was validated empirically using somatic insertions/deletions (indels) from 9,101 the Cancer Genome Atlas (TCGA) samples, showing three-fold higher enrichment in driver genes vs. non-drivers. To explore if the enriched mutant allele expression can be leveraged to complement DNA-based somatic mutation detection, we performed de novo somatic indel calling using TCGA RNA-Seq, increasing the TCGA driver indel repertoire by [~]14%, mostly in samples with lower tumor purity. Our study not only revealed confounding factors in ASE analysis but also demonstrated the utility of leveraging RNA-Seq for driver mutation analysis.

bioinformatics↗

bioRxiv2020

St. Jude Cloud a Pediatric Cancer Genomic Data Sharing Ecosystem

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long-term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem developed via collaboration between St. Jude Childrens Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Judes prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem--Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community--provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

cancer biology↗

bioRxiv2020

MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities (H3K4me3 and H3K27ac enrichment) from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

genomics↗

bioRxiv2018

GRID - Genomics of Rare Immune Disorders: a highly sensitive and specific diagnostic gene panel for patients with primary immunodeficiencies

Primary Immune disorders affect 15,000 new patients every year in Europe. Genetic tests are usually performed on a single or very limited number of genes leaving the majority of patients without a genetic diagnosis. We designed, optimised and validated a new clinical diagnostic platform called GRID, Genomics of Rare Immune Disorders, to screen in parallel 279 genes, including 2015 IUIS genes, known to be causative of Primary Immune disorders (PID). Validation to clinical standard using more than 58,000 variants in 176 PID patients shows an excellent sensitivity, specificity. The customised and automated bioinformatics pipeline prioritises and reports pertinent Single Nucleotide Variants (SNVs), INsertions and DELetions (INDELs) as well as Copy Number Variants (CNVs). An example of the clinical utility of the GRID panel, is represented by a patient initially diagnosed with X-linked agammaglobulinemia due to a missense variant in the BTK gene with severe inflammatory bowel disease. GRID results identified two additional compound heterozygous variants in IL17RC, potentially driving the altered phenotype.

genomics↗