Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institute Pasteur of Tunis, Tunis, Tunisia.
University of Tunis El Manar, Tunis, Tunisia.
PLoS One. 2018 Jul 6;13(7):e0199461. doi: 10.1371/journal.pone.0199461. eCollection 2018.
A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.
慢性炎症状态在很大程度上解释了镰状细胞病（SCD）的病理生理学。然而，影响这一主要途径的主要失调因素及其作用机制仍有待充分确定和阐明。整合基因表达和全基因组关联研究（GWAS）数据分析代表了一种新方法，可以细化复杂疾病中关键介质和功能的识别。在这里，我们对五个与纯合 SS 镰状细胞病患者相关的公开可用的微阵列数据集进行了基因表达荟萃分析，以确定一致的 SCD 转录组图谱。使用 MetaDE R 包基于合并 p 值（maxP 方法）进行的荟萃分析确定了 335 个差异表达基因（DEGs；224 个上调和 111 个下调）。功能基因集富集揭示了几个代谢途径、先天免疫反应、红细胞发育和止血途径的重要性。通过 atSNP R 包和 SIFT 工具在本研究框架内生成的 GWAS 数据的高级分析，确定了 20 个 DEG 启动子中发生的 60 个调节性单核苷酸多态性（rSNP）和一个有害 SNP，影响 CAMKK2 蛋白功能。该候选基因、转录因子和与 SCD 相关的 rSNP 的新数据库提供了新的标记，可能有助于识别新的治疗靶点。
