RESUMEN
Mutations in the X-linked MECP2 cause Rett syndrome, a devastating neurological disorder typified by a period of apparently normal development followed by loss of cognitive and psychomotor skills. Data from rare male patients suggest symptom onset and severity can be influenced by the location of the mutation, with amino acids 270 and 273 marking the difference between neonatal encephalopathy and death, on the one hand, and survival with deficits on the other. We therefore generated two mouse models expressing either MeCP2-R270X or MeCP2-G273X. The mice developed phenotypes at strikingly different rates and showed differential ATRX nuclear localization within the nervous system, over time, coinciding with phenotypic progression. We discovered that MeCP2 contains three AT-hook-like domains over a stretch of 250 amino acids, like HMGA DNA-bending proteins; one conserved AT-hook is disrupted in MeCP2-R270X, lending further support to the notion that one of MeCP2's key functions is to alter chromatin structure.
Asunto(s)
Proteína 2 de Unión a Metil-CpG/química , Proteína 2 de Unión a Metil-CpG/metabolismo , Síndrome de Rett/metabolismo , Secuencia de Aminoácidos , Animales , ADN Helicasas/metabolismo , Modelos Animales de Enfermedad , Femenino , Heterocromatina/metabolismo , Masculino , Proteína 2 de Unión a Metil-CpG/genética , Ratones , Ratones Noqueados , Ratones Transgénicos , Datos de Secuencia Molecular , Proteínas Nucleares/metabolismo , Estructura Terciaria de Proteína , Síndrome de Rett/genética , Síndrome de Rett/fisiopatología , Alineación de Secuencia , Transcripción Genética , Proteína Nuclear Ligada al Cromosoma XRESUMEN
Genome-wide association studies (GWAS) and whole-exome sequencing (WES) generate massive amounts of genomic variant information, and a major challenge is to identify which variations drive disease or contribute to phenotypic traits. Because the majority of known disease-causing mutations are exonic non-synonymous single nucleotide variations (nsSNVs), most studies focus on whether these nsSNVs affect protein function. Computational studies show that the impact of nsSNVs on protein function reflects sequence homology and structural information and predict the impact through statistical methods, machine learning techniques, or models of protein evolution. Here, we review impact prediction methods and discuss their underlying principles, their advantages and limitations, and how they compare to and complement one another. Finally, we present current applications and future directions for these methods in biological research and medical genetics.