基于高通量测序技术的高原肺水肿发生的细胞因子相关基因研究

High altitude pulmonary edema (HAPE) is a non-cardiogenic pulmonary edema caused by high altitude hypoxia environment, which serious threat to the life. HAPE is an idiopathic disease on plateau region, and the consequences are serious characterized. It is still lack of very effective treatment measures for HAPE.Thus, to carry out the study on development mechanism of HAPE and to provide effective technology and methods of detection for its occurrence prevention is extremely urgent. Previous studies showed that HAPE occurrence is the result of genetic and environmental factors. The application of high-throughput sequencing technology offered us an opportunity to study the possible genetic factors for HAPE in the whole genome level. This study intends to study the genetic differences between the occurred HAPE group and normal control group in Chinese Han population using genome-wide exon sequencing technology. Composed with previous results, we hope to find the key genes and mutations that related to HAPE development. At the same time, we will analyze the gene-gene and gene–environment interaction on HAPE occurrence, and establish the genetic model for HAPE prediction. The results will provide early warning index and basis, and also be applied for further HAPE studies, and later establishment of clinical pathway for the effective treatment of HAPE.

高原肺水肿是由于高海拔低氧环境所引起的一种严重威胁生命的非心源性肺水肿，是高原地区的特发病、常见病，以发病急，病情进展迅速、后果严重为特点，目前尚缺乏非常有效的治疗措施。开展HAPE发生的机制研究，为预防其发生提供有效的检测技术和手段显得极为迫切。以往研究结果表明：高原肺水肿是遗传和环境因素共同作用的结果。高通量测序技术的应用，为我们在基因组水平上研究HAPE的遗传因素成为可能。本研究拟通过高通量测序等技术，对中国汉族人群中发生HAPE和正常对照组的细胞因子外显子进行遗传差异分析，结合以往研究，以期明确HAPE发生的细胞因子关键基因及变异位点，同时分析不同基因位点之间与HAPE发生相关的联合作用机制，并建立多基因位点的遗传模型。本课题研究结果预期可应用于可能发生HAPE的人群的提前预测，同时为HAPE的进一步防治研究建立遗传学基础，为后期建立有效的治疗HAPE的临床路径供预警指标及依据。

高原肺水肿发病急、病情进展迅速，开展HAPE发生的机制研究，提供有效预防HAPE发生的检测技术和手段显得极为迫切。本研究通过高通量测序、基因分型技术分析不同基因位点之间与HAPE发生的相关性，探究HAPE发生的遗传学机制，并建立多基因位点的遗传模型。结果发现：1）通过对50例高原肺水肿患者进行高通量测序分析，结果发现了10853个变异。其中超过51.6%的外显子变异组为非同义SNV，根据SNV/InDel位点的突变频率筛选候选基因，确定了可能与HAPE相关的前10个基因（NOS3、TGFA、CFH、NOSTRIN、MYBPC3、AKT1、PIK3R2、NOSIP、ITGAV、ERBB2）。2）分析了代谢相关基因、CYP450家族基因、IL基因、端粒相关基因以及LINC-PINT和LINC00599等候选基因，确定了ACYP2（rs6713088、rs843752、rs11896604、rs12615793和rs17045754）、RTEL1（rs6089953、rs6010621和rs2297441）、AGTR1（rs4524238、rs275651和rs275652）、GSTP1（rs749174）、ADRB2（rs1042718）、GNB3（rs4963516）、IL1R2（rs2072472）、IL6（rs1800796和rs1524107）、CYP2C19（rs4494250）、LINC00599（rs2272026）以及LINC-PINT （rs1962430）11个基因与HAPE患病风险相关。3）利用LASSO回归机器学习算法建立HAPE风险预警模型，该模型AUC值达到80.5%，特异性为75.3%，灵敏度为70.7%。此研究为HAPE的进一步防治建立遗传学基础，为后期建立有效治疗HAPE临床路径提供预警指标及依据。