BackgroundTRNT1 (CCA-adding transfer RNA nucleotidyl transferase) enzyme deficiency is a new metabolic disease caused by defective post-transcriptional modification of mitochondrial and cytosolic transfer RNAs (tRNAs).ResultsWe investigated four patients from two families with infantile-onset cyclical, aseptic febrile episodes with vomiting and diarrhoea, global electrolyte imbalance during these episodes, sideroblastic anaemia, B lymphocyte immunodeficiency, retinitis pigmentosa, hepatosplenomegaly, exocrine pancreatic insufficiency and renal tubulopathy. Other clinical features found in children include sensorineural deafness, cerebellar atrophy, brittle hair, partial villous atrophy and nephrocalcinosis.Whole exome sequencing and bioinformatic filtering were utilised to identify recessive compound heterozygous TRNT1 mutations (missense mutation c.668T>C, p.Ile223Thr and a novel splice mutation c.342+5G>T) segregating with disease in the first family. The second family was found to have a homozygous TRNT1 mutation (c.569G>T), p.Arg190Ile, (previously published).We found normal mitochondrial translation products using passage matched controls and functional perturbation of 3’ CCA addition to mitochondrial tRNAs (tRNACys, tRNALeuUUR and tRNAHis) in fibroblasts from two patients, demonstrating a pathomechanism affecting the CCA addition to mt-tRNAs. Acute management of these patients included transfusion for anaemia, fluid and electrolyte replacement and immunoglobulin therapy. We also describe three-year follow-up findings after treatment by bone marrow transplantation in one patient, with resolution of fever and reversal of the abnormal metabolic profile.ConclusionsOur report highlights that TRNT1 mutations cause a spectrum of disease ranging from a childhood-onset complex disease with manifestations in most organs to an adult-onset isolated retinitis pigmentosa presentation. Systematic review of all TRNT1 cases and mutations reported to date revealed a distinctive phenotypic spectrum and metabolic and other investigative findings, which will facilitate rapid clinical recognition of future cases.

The PCDH19 gene consists of six exons encoding a 1,148 amino acid transmembrane protein, Protocadherin 19, which is involved in brain development. Heterozygous pathogenic variants in this gene are inherited in an unusual X‐linked dominant pattern in which heterozygous females are affected, while hemizygous males are typically unaffected, although they pass on the pathogenic variant to each affected daughter. PCDH19‐related disorder is known to cause early‐onset epilepsy in females characterized by seizure clusters exacerbated by fever and in most cases, onset is within the first year of life. This condition was initially described in 1971 and in 2008 PCDH19 was identified as the underlying genetic etiology. This condition is the result of pathogenic loss‐of‐function variants that may be de novo or inherited from an affected mother or unaffected father and cellular interference has been hypothesized to be the culprit. Heterozygous females are symptomatic because of the presence of both wild‐type and mutant cells that interfere with one another due to the production of different surface proteins, whereas nonmosaic hemizygous males produce a homogenous population of cells. Here, we review novel pathogenic variants in the PCDH19 gene since 2012 to date, and summarize any genotype‐phenotype correlations.

NGS-based CNV detection followed by allele-specific ddPCR confirmatory testing is a reliable and affordable approach for copy number analysis in medically relevant genes with homology issues.

Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies. Despite rapid advancements, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization. Phenoxome dissects the phenotypic manifestation of a patient in concert with their genomic profile to filter and then prioritize variants that are likely to affect the function of the gene (potentially pathogenic variants). To validate our method, we have compiled a clinical cohort of 105 positive patient samples that represent a wide range of genetic heterogeneity. Phenoxome identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these exomes, respectively. Furthermore, we show that our method is optimized for clinical testing by outperforming the current state-of-art method. We have demonstrated the performance of Phenoxome using a clinical cohort and showed that it enables rapid and accurate interpretation of clinical exomes. Phenoxome is available at https://phenoxome.chop.edu/.

Exome-based panels are becoming the preferred diagnostic strategy in clinical laboratories. This approach enables dynamic gene content update and, if needed, cost-effective reflex to whole-exome sequencing. Currently, no guidelines or appropriate resources are available to support the clinical implementation of exome-based panels. Here, we highlight principles and important considerations for the clinical development and validation of exome-based panels. In addition, we developed ExomeSlicer, a novel, web-based resource, which uses empirical exon-level next-generation sequencing quality metrics to predict and visualize technically challenging exome-wide regions in any gene or genes of interest. Exome sequencing data from 100 clinical epilepsy cases were used to illustrate the clinical utility of ExomeSlicer in predicting poor-quality regions and its impact on streamlining the ad hoc Sanger sequencing fill in burden. With the use of ExomeSlicer,>2100 low complexity and/or high-homology regions affecting >1615 genes across the exome were also characterized. These regions can be a source of false-positive or false-negative variant calls, which can lead to misdiagnoses in tested patients and/or inaccurate functional annotations. We provide important considerations and a novel resource for the clinical development of exome-based panels.