Department of Nutrition, Food Science and Physiology, Centre for Nutrition Research, University of Navarra, Irunlarrea 1, 31008, Pamplona, Navarra, Spain.
CIBERobn, CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
Eur J Nutr. 2019 Aug;58(5):1971-1980. doi: 10.1007/s00394-018-1750-x. Epub 2018 Jun 14.
The interindividual variable response to weight-loss treatments requires the search for new predictive biomarkers for improving the success of weight-loss programs. The aim of this study is to identify novel genes that distinguish individual responses to a weight-loss dietary treatment by using the integrative analysis of mRNA expression and DNA methylation arrays.
Subjects from Metabolic Syndrome Reduction in Navarra (RESMENA) project were classified as low (LR) or high (HR) responders depending on their weight loss. Transcriptomic (n = 24) and epigenomic (n = 47) patterns were determined by array-based genome-wide technologies in human white blood cells at the baseline of the treatment period. CD44 expression was validated by qRT-PCR and methylation degree of CpGs of the gene was validated by MassARRAY EpiTYPER™ in a subsample of 47 subjects. CD44 protein levels were measured by ELISA in human plasma.
Different expression and DNA methylation profiles were identified in LR in comparison to HR. The integrative analysis of both array data identified four genes: CD44, ITPR1, MTSS1 and FBXW5 that were differently methylated and expressed between groups. CD44 showed higher expression and lower DNA methylation levels in LR than in HR. Although differences in CD44 protein levels between LR and HR were not statistically significant, a positive association was observed between CD44 mRNA expression and protein levels.
In summary, the combination of a genome-wide methylation and expression array dataset can be a useful strategy to identify novel genes that might be considered as predictors of the dietary response. CD44 gene transcription and methylation may be a possible candidate biomarker for weight-loss prediction.
个体对减肥治疗的反应存在差异,这就需要寻找新的预测生物标志物,以提高减肥计划的成功率。本研究旨在通过整合 mRNA 表达和 DNA 甲基化阵列分析,鉴定出能区分个体对减肥饮食治疗反应的新基因。
代谢综合征减少在纳瓦拉(RESMENA)项目的参与者根据体重减轻情况被分为低(LR)或高(HR)应答者。在治疗期开始时,通过基于阵列的全基因组技术在人类白细胞中确定转录组(n=24)和表观基因组(n=47)图谱。通过 qRT-PCR 验证 CD44 的表达,并在 47 名受试者的亚样本中通过 MassARRAY EpiTYPER™ 验证基因的 CpG 甲基化程度。通过 ELISA 测量人血浆中的 CD44 蛋白水平。
与 HR 相比,LR 存在不同的表达和 DNA 甲基化谱。对两种阵列数据的综合分析确定了四个基因:CD44、ITPR1、MTSS1 和 FBXW5,它们在组间的甲基化和表达存在差异。LR 中的 CD44 表达更高,DNA 甲基化水平更低。虽然 LR 和 HR 之间的 CD44 蛋白水平差异没有统计学意义,但观察到 CD44 mRNA 表达与蛋白水平之间存在正相关。
综上所述,全基因组甲基化和表达阵列数据集的组合可能是一种有用的策略,可以识别可能被认为是饮食反应预测因子的新基因。CD44 基因转录和甲基化可能是预测减肥的潜在候选生物标志物。