Tanimine Naoki, Takei Daisuke, Tsukiyama Naohumi, Yoshinaka Hisaaki, Takemoto Yuki, Tanaka Yuka, Kobayashi Tsuyoshi, Tanabe Kazuaki, Ishikawa Nobuhisa, Kitahara Yoshihiro, Okimoto Mafumi, Shime Nobuaki, Ohge Hiroki, Sugiyama Aya, Akita Tomoyuki, Tanaka Junko, Ohdan Hideki
Department of Gastroenterological and Transplant Surgery, Hiroshima University, Hiroshima, Japan.
Department of Perioperative and Critical Management, Hiroshima University, Hiroshima, Japan.
Crit Care Explor. 2021 Nov 5;3(11):e0576. doi: 10.1097/CCE.0000000000000576. eCollection 2021 Nov.
The pathology caused by the coronavirus disease 2019 is mediated by host-mediated lung inflammation, driving severity, and mortality. Polymorphisms in genes encoding host inflammation and immune-related molecules may be associated with the development of serious pathologies, and identifying such gene polymorphisms may lead to the identification of therapeutic targets.
We attempted to identify aggravation-predicting gene polymorphisms.
We use a candidate gene approach associated with multiple phase pathogenesis in coronavirus disease 2019 patients among a cohort in Hiroshima, a city with a population of 1 million, in Japan. DNA samples from the study populations were genotyped for 34 functional polymorphisms from 14 distinct candidate genes, which encode proteins related to viral cell entry, regulation of viral replication, innate immune modulators, regulatory cytokines, and effector cytokines.
Three core hospitals providing different services for patients with coronavirus disease 2019 under administrative control. A total of 230 patients with coronavirus disease 2019 were recruited from March 1, 2020, to March 31, 2021.
Among the 14 genes, we found rs1131454 in and rs1143627 in genes as independent genetic factors associated with disease severity (adjusted odds ratio = 7.1 and 4.6 in the dominant model, respectively). Furthermore, we investigated the effect of multiple phase pathogenesis of coronavirus disease 2019 with unbiased multifactor dimensionality reduction analysis and identified a four-gene model with rs1131454 (), rs1143627 (), rs2074192 (), and rs11003125 (). By combining these polygenetic factors with polyclinical factors, including age, sex, higher body mass index, and the presence of diabetes and hypertension, we proposed a composite risk model with a high area under the curve, sensitivity, and probability (0.917, 96.4%, and 74.3%, respectively) in the receiver operating characteristic curve analysis.
We successfully identified significant genetic factors in and genes using a candidate gene approach study as valuable information for further mechanistic investigation and predictive model building.
2019冠状病毒病所引发的病理变化是由宿主介导的肺部炎症介导的，这种炎症会导致疾病的严重程度和死亡率上升。编码宿主炎症和免疫相关分子的基因中的多态性可能与严重病理状况的发展有关，识别此类基因多态性可能会有助于确定治疗靶点。
我们试图识别可预测病情加重的基因多态性。
在日本广岛市（人口100万）的一个队列中，我们采用候选基因方法，该方法与2019冠状病毒病患者的多阶段发病机制相关。对研究人群的DNA样本进行基因分型，检测14个不同候选基因中的34个功能多态性，这些基因编码与病毒细胞进入、病毒复制调控、先天免疫调节剂、调节性细胞因子和效应细胞因子相关的蛋白质。
三家核心医院，在行政管控下为2019冠状病毒病患者提供不同服务。2020年3月1日至2021年3月31日期间，共招募了230例2019冠状病毒病患者。
在这14个基因中，我们发现基因中的rs1131454和基因中的rs1143627是与疾病严重程度相关的独立遗传因素（显性模型中调整后的比值比分别为7.1和4.6）。此外，我们通过无偏多因素降维分析研究了2019冠状病毒病多阶段发病机制的影响，并确定了一个包含rs1131454（基因）、rs1143627（基因）、rs2074192（基因）和rs11003125（基因）的四基因模型。通过将这些多基因因素与包括年龄、性别、较高的体重指数以及糖尿病和高血压的存在等多临床因素相结合，我们提出了一个复合风险模型，在受试者工作特征曲线分析中，该模型具有较高的曲线下面积、敏感性和概率（分别为0.917、96.4%和74.3%）。
我们通过候选基因方法研究，成功在基因和基因中识别出显著的遗传因素，这对于进一步的机制研究和预测模型构建是有价值的信息。
