RESUMEN
In this study, methionine sulfoxide (MetO) was identified as an active metabolite that suppresses adipogenesis after screening obese individuals versus the normal population. MetO suppressed the gene and protein expression of CCAAT/enhancer binding protein (C/EBP) α, adipocyte fatty acid binding protein 4 (FABP4), and the nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ) during human preadipocyte (HPA) differentiation. Adipogenesis decreased following MetO treatment; however, the preadipocyte number, proliferation, and apoptosis were unaffected. The activity of phosphorylated extracellular signal-related kinase (P-ERK) of the mitogen-activated protein kinase (MAPK) pathway was significantly inhibited in HPA after MetO treatment. Furthermore, treatment of preadipocytes with the selective P-ERK1/2 agonist Ro 67-7476 abolished the effect of MetO against adipogenesis suggesting that MetO function is dependent on the MAPK pathway. The mechanistic insights of adipogenesis suppression by MetO presented in this study shows its potential as an antiobesity drug.
Asunto(s)
Adipocitos , Adipogénesis , Humanos , Ratones , Animales , Adipocitos/metabolismo , Transducción de Señal , Quinasas MAP Reguladas por Señal Extracelular/metabolismo , Proteína alfa Potenciadora de Unión a CCAAT/genética , Proteína alfa Potenciadora de Unión a CCAAT/metabolismo , Proteína alfa Potenciadora de Unión a CCAAT/farmacología , PPAR gamma/metabolismo , Células 3T3-L1 , Diferenciación CelularRESUMEN
Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.
