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1.
Cell ; 177(3): 597-607.e9, 2019 04 18.
Artículo en Inglés | MEDLINE | ID: mdl-31002796

RESUMEN

The melanocortin 4 receptor (MC4R) is a G protein-coupled receptor whose disruption causes obesity. We functionally characterized 61 MC4R variants identified in 0.5 million people from UK Biobank and examined their associations with body mass index (BMI) and obesity-related cardiometabolic diseases. We found that the maximal efficacy of ß-arrestin recruitment to MC4R, rather than canonical Gαs-mediated cyclic adenosine-monophosphate production, explained 88% of the variance in the association of MC4R variants with BMI. While most MC4R variants caused loss of function, a subset caused gain of function; these variants were associated with significantly lower BMI and lower odds of obesity, type 2 diabetes, and coronary artery disease. Protective associations were driven by MC4R variants exhibiting signaling bias toward ß-arrestin recruitment and increased mitogen-activated protein kinase pathway activation. Harnessing ß-arrestin-biased MC4R signaling may represent an effective strategy for weight loss and the treatment of obesity-related cardiometabolic diseases.


Asunto(s)
Mutación con Ganancia de Función/genética , Obesidad/patología , Receptor de Melanocortina Tipo 4/genética , Transducción de Señal , Adulto , Anciano , Índice de Masa Corporal , Enfermedad de la Arteria Coronaria/complicaciones , Enfermedad de la Arteria Coronaria/metabolismo , Enfermedad de la Arteria Coronaria/patología , AMP Cíclico/metabolismo , Bases de Datos Factuales , Diabetes Mellitus Tipo 2/complicaciones , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/patología , Femenino , Subunidades alfa de la Proteína de Unión al GTP Gs/metabolismo , Predisposición Genética a la Enfermedad , Genotipo , Humanos , Masculino , Persona de Mediana Edad , Obesidad/complicaciones , Obesidad/metabolismo , Polimorfismo de Nucleótido Simple , Receptor de Melanocortina Tipo 4/química , Receptor de Melanocortina Tipo 4/metabolismo , beta-Arrestinas/metabolismo
2.
Nature ; 559(7714): 400-404, 2018 07.
Artículo en Inglés | MEDLINE | ID: mdl-29988082

RESUMEN

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.


Asunto(s)
Predisposición Genética a la Enfermedad , Salud , Leucemia Mieloide Aguda/genética , Mutación , Adulto , Factores de Edad , Anciano , Progresión de la Enfermedad , Registros Electrónicos de Salud , Femenino , Humanos , Leucemia Mieloide Aguda/epidemiología , Leucemia Mieloide Aguda/patología , Masculino , Persona de Mediana Edad , Modelos Genéticos , Mutagénesis , Prevalencia , Medición de Riesgo
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