Team 'From Gametes to Birth', Institut Cochin, U1016 INSERM, UMR 8104 CNRS, Paris-Descartes University, 75014 Paris, France.
Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust, London SW17 0RE, UK.
Cells. 2023 Feb 10;12(4):578. doi: 10.3390/cells12040578.
In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and identified, we found 43 e-genes for which proximal SNPs influence expression level. Then, we performed the analysis again, removing the disease status from the covariates, and we identified 54 e-genes, 16 of which are identified de novo and, thus, possibly related to placental disease. We found a highly significant overlap with previous studies for the list of 43 e-genes, validating our methodology and findings. Among the 16 disease-specific e-genes, several are intrinsic to trophoblast biology and, therefore, constitute novel targets of interest to better characterize placental pathology and its varied clinical consequences. The approach that we used may also be applied to the study of other human diseases where confounding factors have hampered a better understanding of the pathology.
在这项研究中,我们试图在正常和病理情况下寻找影响人类胎盘基因表达的遗传变异(eQTL=表达数量性状基因座)。在胎盘疾病(子痫前期和宫内生长受限)的基因表达分析中,由于与对照样本相比,患病胎盘组织样本通常取自较早的妊娠阶段,这一事实受到阻碍。妊娠年龄的差异被认为是胎盘转录组调节中的一个主要混杂因素。为了缓解这一重大问题,我们在这里提出了一种新的方法来确定特定于疾病的顺式-eQTL。通过对采样时的妊娠年龄以及系统搜索和识别的其他混杂/替代变量进行统计校正,我们确定了 43 个近端 SNP 影响表达水平的 e 基因。然后,我们再次进行了分析,从协变量中删除疾病状态,并确定了 54 个 e 基因,其中 16 个是新发现的,因此可能与胎盘疾病有关。我们发现,对于 43 个 e 基因列表,与之前的研究有高度显著的重叠,验证了我们的方法和发现。在 16 个疾病特异性 e 基因中,有几个是滋养细胞生物学所固有的,因此构成了更好地描述胎盘病理及其各种临床后果的新的感兴趣的目标。我们使用的方法也可以应用于其他人类疾病的研究,这些疾病中的混杂因素阻碍了对病理的更好理解。