Dataset Information

Against all odds: blended phenotypes of three single-gene defects.

ABSTRACT: Whole-exome sequencing allows for an unbiased and comprehensive mutation screening. Although successfully used to facilitate the diagnosis of single-gene disorders, the genetic cause(s) of a substantial proportion of presumed monogenic diseases remain to be identified. We used whole-exome sequencing to examine offspring from a consanguineous marriage featuring a novel combination of congenital hypothyroidism, hypomagnesemia and hypercholesterolemia. Rather than identifying one causative variant, we report the first instance in which three independent autosomal-recessive single-gene disorders were identified in one patient. Together, the causal variants give rise to a blended and seemingly novel phenotype: we experimentally characterized a novel splice variant in the thyroglobulin gene (c.638+5G>A), resulting in skipping of exon 5, and detected a pathogenic splice variant in the magnesium transporter gene TRPM6 (c.2667+1G>A), causing familial hypomagnesemia. Based on the third variant, a stop variant in ABCG5 (p.(Arg446*)), we established a diagnosis of sitosterolemia, confirmed by elevated blood plant sterol levels and successfully initiated targeted lipid-lowering treatment. We propose that blended phenotypes resulting from several concomitant single-gene disorders in the same patient likely account for a proportion of presumed monogenic disorders of currently unknown cause and contribute to variable genotype-phenotype correlations.

SUBMITTER: Li Y

PROVIDER: S-EPMC4989199 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Against all odds: blended phenotypes of three single-gene defects.

Li Yong Y Salfelder Anika A Schwab Karl Otfried KO Grünert Sarah Catharina SC Velten Tanja T Lütjohann Dieter D Villavicencio-Lorini Pablo P Matysiak-Scholze Uta U Zabel Bernhard B Köttgen Anna A Lausch Ekkehart E

European journal of human genetics : EJHG 20160127 9

Whole-exome sequencing allows for an unbiased and comprehensive mutation screening. Although successfully used to facilitate the diagnosis of single-gene disorders, the genetic cause(s) of a substantial proportion of presumed monogenic diseases remain to be identified. We used whole-exome sequencing to examine offspring from a consanguineous marriage featuring a novel combination of congenital hypothyroidism, hypomagnesemia and hypercholesterolemia. Rather than identifying one causative variant, ...[more]

PMID: 26813946

Similar Datasets

Project description:Recent analysis of single-cell transcriptomic data has revealed a surprising organization of the transcriptional variability pervasive across individual neurons. In response to distinct combinations of synaptic input-type, a new organization of neuronal subtypes emerged based on transcriptional states that were aligned along a gradient of correlated gene expression. Individual neurons traverse across these transcriptional states in response to cellular inputs. However, the regulatory network interactions driving these changes remain unclear. Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. Our approach involves developing an a priori regulatory network that is then trained against in vivo single-cell gene expression data in order to identify causal gene interactions and corresponding quantitative model parameters. Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably well. In addition, the network identification results revealed that distinct regulatory interactions, coupled with differences in the regulatory network stimuli, drive the variable gene expression patterns observed across the neuronal subtypes. We also identified a key difference between the neuronal subtype-specific networks with respect to negative feedback regulation, with the catecholaminergic subtype network lacking such interactions. Furthermore, by varying regulatory network stimuli over a wide range, we identified several cases in which divergent neuronal subtypes could be driven towards similar transcriptional states by distinct stimuli operating on subtype-specific regulatory networks. Based on these results, we conclude that heterogenous single-cell gene expression profiles should be interpreted through a regulatory network modeling perspective in order to separate the contributions of network interactions from those of cellular inputs.

Dataset Information

Against all odds: blended phenotypes of three single-gene defects.

Publications

Against all odds: blended phenotypes of three single-gene defects.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets