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Blumenfeld, A.

Publications and source records attributed to Blumenfeld, A..

2 recordsLinked to original sources

Steric Interactions at Gln154 in ZEITLUPE Induce Reorganization of the LOV Domain Dimer Interface

Plants measure light, quality, intensity, and duration to coordinate growth and development with daily and seasonal changes in environmental conditions, however, the molecular details linking photochemistry to signal transduction remain incomplete. Two closely related Light, Oxygen, or Voltage (LOV) domain containing photoreceptor proteins ZEITLUPE (ZTL) and FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1) divergently regulate the protein stability of circadian clock and photoperiodic flowering components to mediate daily and seasonal development. Using structural approaches, we identified that mutations at the Gly46 position led to global rearrangements of the ZTL dimer interface. Specifically, introduction of G46S and G46A variants that mimic equivalent residues found in FKF1 induce a 180{degrees} rotation about the dimer interface that is coupled to ordering of N- and C-terminal signaling elements. These conformational changes hinge upon rotation of a C-terminal Gln residue analogous to that present in light-state structures of ZTL. The results presented herein, confirm a divergent signaling mechanism within ZTL that deviates from other members of the LOV superfamily and suggests that mechanisms of signal transduction in LOV proteins may be fluid across the LOV protein family.

biophysics

Validating gene-phenotype associations using relationships in the UMLS

ObjectiveLarge scale next-generation sequencing of population cohorts paired with patients electronic health records (EHR) provides an excellent resource for the study of gene-disease associations. To validate those associations, researchers often consult databases that identify relationships between genes of interest and relevant disease phenotypes, which we refer to as simply "phenotypes". However, most of these databases contain phenotypes that are not suited for automated analysis of EHR data, which often captured these phenotypes in the form of International Classification of Diseases (ICD) codes. There is a need for a resource that comprehensively provides gene-phenotype mappings in a format that can be used to evaluate phenotypes from EHR. MethodsWe built a directed graph database of genes, medical concepts and ICD codes based on a subset of the National Library of Medicines Unified Medical Language System (UMLS) and other resources. To obtain associations between genes and ICD codes, we traversed the defined relationships from gene, variant and disease concepts to ICD codes, resulting in a set of mappings that link specific genes and variants to these ICD codes. ResultsOur method created 249,764 mappings between genes and ICD codes, including 27,226 "disease" phenotypes and 222,538 "symptom" phenotypes, and provided mappings for 4,456 unique genes. Paths were validated by manual review of a diverse sample of paths. In a cohort of 92,455 samples, we used these mappings to validate gene-phenotype associations in 32,786 samples where a person had a potentially disease-causing genetic mutation and at least one corresponding diagnosis in their EHR. ConclusionThe concepts and relationships in the UMLS can be used to generate gene-ICD phenotype mappings that are not explicit in the source vocabularies. We were able use these mappings to validate gene-disease associations in a large cohort of sequenced exomes paired with EHR.

bioinformatics