Although protein hydroxylation is a relatively poorly characterized post-translational modification, it has received significant recent attention following seminal work uncovering its role in oxygen sensing and hypoxia biology. Although the fundamental importance of protein hydroxylases in biology is becoming clear, the biochemical targets and cellular functions often remain enigmatic. JMJD5 is a 'JmjC-only' protein hydroxylase that is essential for murine embryonic development and viability. However, no germline variants in JmjC-only hydroxylases, including JMJD5, have yet been described that are associated with any human pathology. Here we demonstrate that biallelic germline JMJD5 pathogenic variants are deleterious to JMJD5 mRNA splicing, protein stability, and hydroxylase activity, resulting in a human developmental disorder characterised by severe failure to thrive, intellectual disability, and facial dysmorphism. We show that the underlying cellular phenotype is associated with increased DNA replication stress and that this is critically dependent on the protein hydroxylase activity of JMJD5. This work contributes to our growing understanding of the role and importance of protein hydroxylases in human development and disease.

Introduction: Epilepsy is a common central nervous system disorder characterized by abnormal brain electrical activity. We aimed to compare the metabolic profiles of plasma from patients with epilepsy across different etiologies, seizure frequency, seizure type, and patient age to try to identify common disrupted pathways. Material and methods: We used data from three separate cohorts. The first cohort (PED-C) consisted of 31 pediatric patients with suspicion of a genetic disorder with unclear etiology; the second cohort (AD-C) consisted of 250 adults from the Estonian Biobank (EstBB), and the third cohort consisted of 583 adults ≥ 69 years of age from the EstBB (ELD-C). We compared untargeted metabolomics and lipidomics data between individuals with and without epilepsy in each cohort. Results: In the PED-C, significant alterations (p-value <0.05) were detected in sixteen different glycerophosphatidylcholines (GPC), dimethylglycine and eicosanedioate (C20-DC). In the AD-C, nine significantly altered metabolites were found, mainly triacylglycerides (TAG), which are also precursors in the GPC synthesis pathway. In the ELD-C, significant changes in twenty metabolites including multiple TAGs were observed in the metabolic profile of participants with previously diagnosed epilepsy. Pathway analysis revealed that among the metabolites that differ significantly between epilepsy-positive and epilepsy-negative patients in the PED-C, the lipid superpathway (p = 3.2*10-4) and phosphatidylcholine (p = 9.3*10-8) and lysophospholipid (p = 5.9*10-3) subpathways are statistically overrepresented. Analogously, in the AD-C, the triacylglyceride subclass turned out to be statistically overrepresented (p = 8.5*10-5) with the lipid superpathway (p = 1.4*10-2). The presented p-values are FDR-corrected. Conclusion: Our results suggest that cell membrane fluidity may have a significant role in the mechanism of epilepsy, and changes in lipid balance may indicate epilepsy. However, further studies are needed to evaluate whether untargeted metabolomics analysis could prove helpful in diagnosing epilepsy earlier. Keywords: epilepsy, metabolomics, lipidomics, phosphatidylcholines

Background: Compared to exome sequencing, genome sequencing is widely appreciated for its superior ability to detect a wide range of genetic variations including copy-number variants (CNVs) and mitochondrial (mtDNA) variants. We assessed whether low-coverage genome sequencing, a considerably cheaper approach, would detect clinically relevant CNVs and mtDNA variants and would thus be a cost-efficient supplement to exome sequencing in rare disease diagnostics. Methods: To assess the level of sequencing depth needed for variant detection, first, 30x mean coverage genome sequencing data were subsampled to 0.5x, 1x, 2x, and 4x coverage files in silico followed by CNV and mtDNA detection. Based on the analysis, 2x short-read sequencing was selected to be performed in 16 patients with putatively pathogenic CNVs or mtDNA variants to assess the empirical sensitivity. Results: For CNV calling, 2x coverage was sufficient to detect all heterozygous CNVs greater than 10kb in size from in silico subsampled data. In experimental data, the results were similar, although a 16kb heterozygous deletion was once not detected. Regarding mtDNA variants, 2x coverage sufficed for variant confident variant calling and heteroplasmy assessment for all samples. Conclusions: Low-coverage genome sequencing may be used to complement exome sequencing for simultaneous mtDNA variant and CNV detection.