Dataset Information

Mutations Impairing GSK3-Mediated MAF Phosphorylation Cause Cataract, Deafness, Intellectual Disability, Seizures, and a Down Syndrome-like Facies.

ABSTRACT: Transcription factors operate in developmental processes to mediate inductive events and cell competence, and perturbation of their function or regulation can dramatically affect morphogenesis, organogenesis, and growth. We report that a narrow spectrum of amino-acid substitutions within the transactivation domain of the v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog (MAF), a leucine zipper-containing transcription factor of the AP1 superfamily, profoundly affect development. Seven different de novo missense mutations involving conserved residues of the four GSK3 phosphorylation motifs were identified in eight unrelated individuals. The distinctive clinical phenotype, for which we propose the eponym Aymé-Gripp syndrome, is not limited to lens and eye defects as previously reported for MAF/Maf loss of function but includes sensorineural deafness, intellectual disability, seizures, brachycephaly, distinctive flat facial appearance, skeletal anomalies, mammary gland hypoplasia, and reduced growth. Disease-causing mutations were demonstrated to impair proper MAF phosphorylation, ubiquitination and proteasomal degradation, perturbed gene expression in primary skin fibroblasts, and induced neurodevelopmental defects in an in vivo model. Our findings nosologically and clinically delineate a previously poorly understood recognizable multisystem disorder, provide evidence for MAF governing a wider range of developmental programs than previously appreciated, and describe a novel instance of protein dosage effect severely perturbing development.

SUBMITTER: Niceta M

PROVIDER: S-EPMC4570552 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mutations Impairing GSK3-Mediated MAF Phosphorylation Cause Cataract, Deafness, Intellectual Disability, Seizures, and a Down Syndrome-like Facies.

Niceta Marcello M Stellacci Emilia E Gripp Karen W KW Zampino Giuseppe G Kousi Maria M Anselmi Massimiliano M Traversa Alice A Ciolfi Andrea A Stabley Deborah D Bruselles Alessandro A Caputo Viviana V Cecchetti Serena S Prudente Sabrina S Fiorenza Maria T MT Boitani Carla C Philip Nicole N Niyazov Dmitriy D Leoni Chiara C Nakane Takaya T Keppler-Noreuil Kim K Braddock Stephen R SR Gillessen-Kaesbach Gabriele G Palleschi Antonio A Campeau Philippe M PM Lee Brendan H L BH Pouponnot Celio C Stella Lorenzo L Bocchinfuso Gianfranco G Katsanis Nicholas N Sol-Church Katia K Tartaglia Marco M

American journal of human genetics 20150409 5

Transcription factors operate in developmental processes to mediate inductive events and cell competence, and perturbation of their function or regulation can dramatically affect morphogenesis, organogenesis, and growth. We report that a narrow spectrum of amino-acid substitutions within the transactivation domain of the v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog (MAF), a leucine zipper-containing transcription factor of the AP1 superfamily, profoundly affect development. Seven ...[more]

PMID: 25865493

Similar Datasets

Project description:Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible. The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID. We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects. The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS. In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets.

Dataset Information

Mutations Impairing GSK3-Mediated MAF Phosphorylation Cause Cataract, Deafness, Intellectual Disability, Seizures, and a Down Syndrome-like Facies.

Publications

Mutations Impairing GSK3-Mediated MAF Phosphorylation Cause Cataract, Deafness, Intellectual Disability, Seizures, and a Down Syndrome-like Facies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets