Targeted RNAseq Improves Clinical Diagnosis of Very Early-Onset Pediatric Immune Dysregulation

A personalized metabolic modelling approach through integrated analysis of RNA-Seq-based genomic variants and gene expression levels in Alzheimer's disease

Generating condition-specific metabolic models by mapping gene expression data to genome-scale metabolic models (GEMs) is a routine approach to elucidate disease mechanisms from a metabolic perspective. On the other hand, integrating variants that perturb enzyme functionality from the same RNA-seq data may enhance GEM accuracy, offering insights into genome-wide metabolic pathology. Our study pioneers the extraction of both transcriptomic and genomic data from the same RNA-seq data to reconstruct personalized metabolic models. We map genes with significantly higher load of pathogenic variants in Alzheimer's disease (AD) onto a human GEM together with the gene expression data. Comparative analysis of the resulting personalized patient metabolic models with the control models shows enhanced accuracy in detecting AD-associated metabolic pathways compared to the case where only expression data is mapped on the GEM. Besides, several otherwise would-be missed pathways are annotated in AD by considering the effect of genomic variants.

2q33 Deletions Underlying Syndromic and Non-syndromic CTLA4 Deficiency

PURPOSE

CTLA4 deficiency is an inborn error of immunity (IEI) due to heterozygosity for germline loss-of-function variants of the CTLA4 gene located on chromosome 2q33.2. CTLA4 deficiency underlies pleiotropic immune and lymphoproliferation-mediated features with incomplete penetrance. It has been identified in hundreds of patients but copy number variants (CNVs) have been reported in only 12 kindreds, including nine which displayed large 2q33.1-2q33.2 deletions encompassing CTLA4.

METHODS

We conducted a nationwide study in France to identify patients with 2q33 deletions encompassing CTLA4. We investigated the clinical and immunological phenotypes and genotypes of these patients.

RESULTS

We identified 12 patients across six unrelated kindreds with clinical immunodeficiency. Neurological features were recorded in three patients, including one with syndromic neurodevelopmental disorder. Single-nucleotide polymorphism (SNP) or comparative genomic hybridization (CGH) array analysis, and targeted high-throughput sequencing revealed five different heterozygous 2q33 deletions of 26 kilobases to 7.12 megabases in size and encompassing one to 41 genes. We identified a contiguous gene syndrome (CGS) due to associated KLF7 deficiency in a kindred with a neurodevelopmental phenotype.

CONCLUSION

Deletions within the 2q33 region encompassing CTLA4 are rare and not extensively explored, and are probably underdiagnosed in cytogenetic practice. A literature review identified 14 different CGS loci including at least one gene responsible for an IEI. The deletions involved in IEIs should be systematically delimited, to facilitate screening for CGS.

RNA Sequencing in Disease Diagnosis

RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.

Genetically transitional disease: a new concept in genomic medicine

Traditional classification of genetic diseases as monogenic and polygenic has lagged far behind scientific progress. In this opinion article, we propose and define a new terminology, genetically transitional disease (GTD), referring to cases where a large-effect mutation is necessary, but not sufficient, to cause disease. This leads to a working disease nosology based on gradients of four types of genetic architecture: monogenic, polygenic, GTD, and mixed. We present four scenarios under which GTD may occur; namely, subsets of traditionally Mendelian disease, modifiable Tier 1 monogenic conditions, variable penetrance, and situations where a genetic mutational spectrum produces qualitatively divergent pathologies. The implications of the new nosology in precision medicine are discussed, in which therapeutic options may target the molecular cause or the disease phenotype.