Sijae Woo

Abstract Splice-switching antisense oligonucleotides (ASOs) could be used to treat a subset of individuals with genetic diseases 1 , but the systematic identification of such individuals remains a challenge. Here we performed whole-genome sequencing analyses to characterize genetic variation in 235 individuals (from 209 families) with ataxia-telangiectasia, a severely debilitating and life-threatening recessive genetic disorder 2,3 , yielding a complete molecular diagnosis in almost all individuals. We developed a predictive taxonomy to assess the amenability of each individual to splice-switching ASO intervention; 9% and 6% of the individuals had variants that were ‘probably’ or ‘possibly’ amenable to ASO splice modulation, respectively. Most amenable variants were in deep intronic regions that are inaccessible to exon-targeted sequencing. We developed ASOs that successfully rescued mis-splicing and ATM cellular signalling in patient fibroblasts for two recurrent variants. In a pilot clinical study, one of these ASOs was used to treat a child who had been diagnosed with ataxia-telangiectasia soon after birth, and showed good tolerability without serious adverse events for three years. Our study provides a framework for the prospective identification of individuals with genetic diseases who might benefit from a therapeutic approach involving splice-switching ASOs.

Background Whole-exome sequencing-based diagnosis of rare diseases typically yields 40%–50% of success rate. Precise diagnosis of the patients with neuromuscular disorders (NMDs) has been hampered by locus heterogeneity or phenotypic heterogeneity. We evaluated the utility of transcriptome sequencing as an independent approach in diagnosing NMDs. Methods The RNA sequencing (RNA-Seq) of muscle tissues from 117 Korean patients with suspected Mendelian NMD was performed to evaluate the ability to detect pathogenic variants. Aberrant splicing and CNVs were inspected to identify additional causal genetic factors for NMD. Aberrant splicing events in Dystrophin (DMD) were investigated by using antisense oligonucleotides (ASOs). A non-negative matrix factorisation analysis of the transcriptome data followed by cell type deconvolution was performed to cluster samples by expression-based signatures and identify cluster-specific gene ontologies. Results Our pipeline called 38.1% of pathogenic variants exclusively from the muscle transcriptomes, demonstrating a higher diagnostic rate than that achieved via exome analysis (34.9%). The discovery of variants causing aberrant splicing allowed the application of ASOs to the patient-derived cells, providing a therapeutic approach tailored to individual patients. RNA-Seq data further enabled sample clustering by distinct gene expression profiles that corresponded to clinical parameters, conferring additional advantages over exome sequencing. Conclusion The RNA-Seq-based diagnosis of NMDs achieves an increased diagnostic rate and provided pathogenic status information, which is not easily accessible through exome analysis.

A code property graph (CPG) is a joint representation of syntax, control flows, and data flows of a target application. Recent studies have demonstrated the promising efficacy of leveraging CPGs for the identification of vulnerabilities. It recasts the problem of implementing a specific static analysis for a target vulnerability as a graph query composition problem. It requires devising coarse-grained graph queries that model vulnerable code patterns. Unfortunately, such coarse-grained queries often leave vulnerabilities due to faulty input sanitization undetected. In this paper, we propose, a scalable system designed to identify various web vulnerabilities, including bugs that stem from incorrect sanitization. We designed to find a subgraph in a target CPG that matches a given CPG query having a known vulnerability, which is known as the subgraph isomorphism problem. To address the scalability challenge that stems from the NP-complete nature of this problem, leverages optimization techniques designed to boost the efficiency of matching vulnerable subgraphs. found confirmed vulnerabilities including CVEs among 2,464 potential vulnerabilities in real-world CPGs having a combined total of 1 billion nodes and 1.2 billion edges.

This RNA-seq dataset was generated and analyzed as described in Kim, Woo, de Gusmao, Zhao et al, "A Framework for Individualized Splice-Switching Oligonucleotide Therapy" Nature 2023. The RNA-seq dataset was obtained from cells derived from ataxia-telangiectasia (A-T) patients or their parents, and it comprises four groups: (1) RNA-seq of fibroblasts from an A-T patient with c.7865C>T (proband) and her mother and father. (2) RNA-seq of fibroblasts from an A-T patient with c.7865C>T (proband), transfected with ASOs (mock, NT-22, AT008, AT026). (3) RNA-seq of whole blood from an A-T patient with c.5763-1050A>G (DDP_ATCP_42) and three other A-T patients without the variant (DDP_ATCP_96, DDP_ATCP_103, DDP_ATCP_151). (4) RNA-seq of fibroblasts from an A-T patient with c.5763-1050A>G (DDP_ATCP_42), transfected with ASOs (naive, NT-22, AT043, AT056, AT057).