J Inherit Metab Dis. 2020 Sep;43(5):1024-1036. doi: 10.1002/jimd.12232. Epub 2020 Apr 15. ABSTRACT Hydrogen sulfide, a signaling molecule formed mainly from cysteine, is catabolized by sulfide:quinone oxidoreductase (gene SQOR). Toxic hydrogen sulfide exposure inhibits complex IV. We describe children of two families with pathogenic variants in SQOR. Exome sequencing identified variants; SQOR enzyme activity was measured spectrophotometrically, protein levels evaluated by western blotting, and mitochondrial function was assayed. In family A, following a brief illness, a 4-year-old girl presented comatose with lactic acidosis and multiorgan failure. After stabilization, she remained comatose, hypotonic, had neurostorming episodes, elevated lactate, and Leigh-like lesions on brain imaging. She died shortly after. Her 8-year-old sister presented with a rapidly fatal episode of coma with lactic acidosis, and lesions in the basal ganglia and left cortex. Muscle and liver tissue had isolated decreased complex IV activity, but normal complex IV protein levels and complex formation. Both patients were homozygous for c.637G > A, which we identified as a founder mutation in the Lehrerleut Hutterite with a carrier frequency of 1 in 13. The resulting p.Glu213Lys change disrupts hydrogen bonding with neighboring residues, resulting in severely reduced SQOR protein and enzyme activity, whereas sulfide generating enzyme levels were unchanged. In family B, a boy had episodes of encephalopathy and basal ganglia lesions. He was homozygous for c.446delT and had severely reduced fibroblast SQOR enzyme activity and protein levels. SQOR dysfunction can result in hydrogen sulfide accumulation, which, consistent with its known toxicity, inhibits complex IV resulting in energy failure. In conclusion, SQOR deficiency represents a new, potentially treatable, cause of Leigh disease. PMID:32160317 | PMC:PMC7484123 | DOI:10.1002/jimd.12232

Hum Genet. 2020 May;139(5):569-574. doi: 10.1007/s00439-020-02135-5. Epub 2020 Feb 13. ABSTRACT Extremely rare diseases are increasingly recognized due to wide-spread, inexpensive genomic sequencing. Understanding the incidence of rare disease is important for appreciating its health impact and allocating recourses for research. However, estimating incidence of rare disease is challenging because the individual contributory alleles are, themselves, extremely rare. We propose a new method to determine incidence of rare, severe, recessive disease in non-consanguineous populations that use known allele frequencies, estimate the combined allele frequency of observed alleles and estimate the number of causative alleles that are thus far unobserved in a disease cohort. Experiments on simulated and real data show that this approach is a feasible method to estimate the incidence of rare disease in European populations but due to several limitations in our ability to assess the full spectrum of pathogenic mutations serves as a useful tool to provide a lower threshold on disease incidence. PMID:32056000 | PMC:PMC7176520 | DOI:10.1007/s00439-020-02135-5

Sci Transl Med. 2019 Apr 24;11(489):eaat6177. doi: 10.1126/scitranslmed.aat6177. ABSTRACT By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children’s deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient’s CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient’s genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments. PMID:31019026 | DOI:10.1126/scitranslmed.aat6177