Pharmacogenomic incidental findings in 308 families: The NIH Undiagnosed Diseases Program experience.

PURPOSE: Using single-nucleotide polymorphism (SNP) chip and exome sequence data from individuals participating in the National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP), we evaluated the number and therapeutic informativeness of incidental pharmacogenetic variants. METHODS: Pharmacogenomics Knowledgebase (PharmGKB) annotated sequence variants were identified in 1,101 individuals. Medication records of participants were used to identify individuals prescribed medications with a genetic variant that might alter efficacy. RESULTS: A total of 395 sequence variants, including 19 PharmGKB 1A and 1B variants, were identified in SNP chip sequence data, and 388 variants, including 21 PharmGKB 1A and 1B variants, were identified in the exome sequence data. Nine participants had incidental pharmacogenetic variants associated with altered efficacy of a prescribed medication. CONCLUSIONS: Despite the small size of the NIH UDP patient cohort, we identified pharmacogenetic incidental findings potentially useful for guiding therapy. Consequently, groups conducting clinical genomic studies might consider reporting of pharmacogenetic incidental findings.Genet Med 18 12, 1303-1307.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

PURPOSE: Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles. METHODS: Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease-gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein-protein association neighbors. RESULTS: Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease-gene associations and ranked the correct seeded variant in up to 87% when detectable disease-gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation. CONCLUSION: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608-617.

CXCR3 and heparin binding sites of the chemokine IP-10 (CXCL10).

The chemokine IP-10 (interferon-inducible protein of 10 kDa, CXCL10) binds the G protein-coupled receptor CXCR3, which is found mainly on activated T cells and NK cells, and plays an important role in Th1-type inflammatory diseases. IP-10 also binds to glycosaminoglycans (GAGs), an interaction thought to be important for its sequestration on endothelial and other cells. In this study, we performed an extensive mutational analysis to identify the CXCR3 and heparin binding sites of murine IP-10. The mutants were characterized for heparin binding, CXCR3 binding, and the ability to induce chemotaxis, Ca(2+) flux, and CXCR3 internalization. Double mutations neutralizing adjacent basic residues at the C terminus did not lead to a significant reduction in heparin binding, indicating that the main heparin binding site of IP-10 is not along the C-terminal alpha helix. Alanine exchange of Arg-22 had the largest effect on heparin binding, with residues Arg-20, Ile-24, Lys-26, Lys-46, and Lys-47 further contributing to heparin binding. A charge change mutation of Arg-22 resulted in further reduction in heparin binding. The N-terminal residue Arg-8, preceding the first cysteine, was critical for CXCR3 signaling. Mutations of charged and uncharged residues in the loop regions of residues 20-24 and 46-47, which caused reduced heparin binding, also resulted in reduced CXCR3 binding and signaling. CXCR3 expressing GAG-deficient Chinese hamster ovary cells revealed that GAG binding was not required for IP-10 binding and signaling through CXCR3, which suggests that the CXCR3 and heparin binding sites of IP-10 are partially overlapping.