Zhang Xiuzhi, Li Ningning, Cui Yanan, Wu Hui, Jiao Jie, Yu Yue, Gu Guizhen, Chen Guoshun, Zhang Huanling, Yu Shanfa
Department of Pathology, Henan Medical College, Zhengzhou, Henan, China.
Department of Scientific Research and Foreign Affairs, Henan Medical College, Zhengzhou, Henan, China.
Front Mol Biosci. 2022 Aug 19;9:907832. doi: 10.3389/fmolb.2022.907832. eCollection 2022.
Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, = 30) and without NIHL (non-NIHL group, = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of < 0.05 and the variable importance of projection (VIP) value >1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite's profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.
噪声暴露可导致多种疾病。噪声性听力损失(NIHL)是困扰噪声暴露工人的主要疾病之一。识别NIHL标志物以进行早期诊断并确定新的治疗靶点以进行治疗至关重要。在本研究中,共收集了60名噪声暴露的钢铁厂男性工人(噪声组)的90份血浆样本,其中有NIHL的(NIHL组,n = 30)和无NIHL的(非NIHL组,n = 30),以及30名无噪声暴露的男性对照(对照组)。采用HPLC-MS/MS测定非靶向人体血浆代谢组学图谱。将样本中代谢物的水平归一化为总峰强度,并对处理后的数据进行多变量数据分析。进行了Wilcoxon检验和正交偏最小二乘判别分析(OPLS-DA)。以P < 0.05和变量投影重要性(VIP)值>1为阈值,鉴定出469种与噪声暴露相关的差异血浆代谢物(DMs-NE),并指出了它们相关的58条KEGG通路。总共鉴定出33种与NIHL相关的差异代谢物(DMs-NIHL)及其相关的12条KEGG通路。有6条与噪声暴露和NIHL都相关的共同通路。通过多重比较,与其他两组相比,NIHL组中有7种代谢物失调。通过LASSO回归分析,构建了两个用于NIHL状态预测的风险模型,其曲线下面积(AUC)值分别为0.840和0.872,能够区分NIHL工人和非NIHL工人,表明它们在NIHL诊断中的有效性。为了验证代谢组学的结果,对来自基因表达综合数据库(GEO)的GSE8342数据集中易感小鼠和抗性小鼠的耳蜗基因表达进行了比较。免疫反应和细胞死亡相关过程因其与噪声暴露的密切关系而受到关注,表明它们在噪声诱导的疾病中起关键作用。我们得出结论,NIHL病例和非NIHL个体之间的代谢物谱存在显著差异。噪声暴露可导致多种生物通路失调,尤其是免疫反应和细胞死亡相关过程。我们的结果可能为噪声暴露研究和NIHL诊断提供新线索。