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DAX1 mutations map to putative structural domains in a deduced three-dimensional model.

ABSTRACT: The DAX1 protein is an orphan nuclear hormone receptor based on sequence similarity in the putative ligand-binding domain (LBD). DAX1 mutations result in X-linked adrenal hypoplasia congenita (AHC). Our objective was to identify DAX1 mutations in a series of families, to determine the types of mutations resulting in AHC and to locate single-amino-acid changes in a DAX1 structural model. The 14 new mutations identified among our 17 families with AHC brought the total number of families with AHC to 48 and the number of reported mutations to 42; 1 family showed gonadal mosaicism. These mutations included 23 frameshift, 12 nonsense, and six missense mutations and one single-codon deletion. We mapped the seven single-amino-acid changes to a homology model constructed by use of the three-dimensional crystal structures of the thyroid-hormone receptor and retinoid X receptor alpha. All single-amino-acid changes mapped to the C-terminal half of the DAX1 protein, in the conserved hydrophobic core of the putative LBD, and none affected residues expected to interact directly with a ligand. We conclude that most genetic alterations in DAX1 are frameshift or nonsense mutations and speculate that the codon deletion and missense mutations give insight into the structure and function of DAX1.

SUBMITTER: Zhang YH

PROVIDER: S-EPMC1377022 | biostudies-other | 1998 Apr

REPOSITORIES: biostudies-other

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DAX1 mutations map to putative structural domains in a deduced three-dimensional model.

Zhang Y H YH Guo W W Wagner R L RL Huang B L BL McCabe L L Vilain E E Burris T P TP Anyane-Yeboa K K Burghes A H AH Chitayat D D Chudley A E AE Genel M M Gertner J M JM Klingensmith G J GJ Levine S N SN Nakamoto J J New M I MI Pagon R A RA Pappas J G JG Quigley C A CA Rosenthal I M IM Baxter J D JD Fletterick R J RJ McCabe E R ER

American journal of human genetics 19980401 4

The DAX1 protein is an orphan nuclear hormone receptor based on sequence similarity in the putative ligand-binding domain (LBD). DAX1 mutations result in X-linked adrenal hypoplasia congenita (AHC). Our objective was to identify DAX1 mutations in a series of families, to determine the types of mutations resulting in AHC and to locate single-amino-acid changes in a DAX1 structural model. The 14 new mutations identified among our 17 families with AHC brought the total number of families with AHC t ...[more]

PMID: 9529340

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Project description:PurposeTo develop and apply a neural network for quantification of endothelial corneal graft detachment using anterior segment (AS) OCT.DesignTraining and validation of a neural network and application within a prospective cohort.ParticipantsPatients two weeks after Descemet membrane endothelial keratoplasty.MethodsInvestigators manually labeled the posterior cornea and the graft in cross-sectional images of rotational AS OCT scans. Neural networks for image segmentation were trained to identify the area of graft detachment on cross-sectional images. The best-performing neural network with the lowest misclassification (Youden index) and highest spatial overlap with the ground truth (Dice coefficient) was selected and evaluated in a separate dataset. Three-dimensional maps of the area and volume of graft detachment were calculated. For application, the neural network's rating on the detachment was compared with slit-lamp-based ratings of cornea specialists on the same day as the AS OCT imaging took place.Main outcome measuresYouden index and Dice coefficient.ResultsNeural networks were trained on 27 AS OCT scans with 6912 labeled images. Among 48 combinations of probability thresholds and epoch states, the best-performing neural network showed a Youden index of 0.99 and a Dice coefficient of 0.77, indicating low misclassification and good spatial overlap on individual image segmentation. In the validation set unknown to the neural network with 20 scans (5120 images), the Youden index was 0.85 and the Dice coefficient was 0.73, and a high overall performance compared with the manually labeled ground truth (R 2 = 0.90). In the application set with 107 eyes, the neural network estimated the mean percent detachment larger than the cornea specialist (mean difference, 8.2 percentage points; 95% confidence interval, 6.2-10.2). Masked review of 42 AS OCTs with more than ±10 percentage points difference in ratings showed that clinicians underestimated the true detachment in cases with significant detachment requiring intervention.ConclusionsDeep learning-based segmentation of AS OCT images quantified the percent and the volume of DMEK graft detachment with high precision. Fully automated 3-dimensional quantification of graft detachment is highly sensitive, particularly in corneas with a significant amount of detachment, and may support decision making.

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DAX1 mutations map to putative structural domains in a deduced three-dimensional model.

Publications

DAX1 mutations map to putative structural domains in a deduced three-dimensional model.

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