Laboratorio de Bacteriología, Departamento de Bioquímica Clínica, Hospital de Clínicas "José de San Martín", Facultad de Farmacia y Bioquímica, UBA, Av. Córdoba 2351, C1120, CABA, Argentina.
Consejo Nacional de Investigaciones Científicas Y Técnicas (CONICET), Godoy Cruz 2290, C1425FQB, CABA, Argentina.
Sci Rep. 2022 Jul 6;12(1):11469. doi: 10.1038/s41598-022-15792-5.
Sepsis has been called the graveyard of pharmaceutical companies due to the numerous failed clinical trials. The lack of tools to monitor the immunological status in sepsis constrains the development of therapies. Here, we evaluated a test based on whole plasma peptidome acquired by MALDI-TOF-mass spectrometer and machine-learning algorithms to discriminate two lipopolysaccharide-(LPS) induced murine models emulating the pro- and anti-inflammatory/immunosuppression environments that can be found during sepsis. The LPS group was inoculated with a single high dose of LPS and the IS group was subjected to increasing doses of LPS, to induce proinflammatory and anti-inflammatory/immunosuppression profiles respectively. The LPS group showed leukopenia and higher levels of cytokines and tissue damage markers, and the IS group showed neutrophilia, lymphopenia and decreased humoral response. Principal component analysis of the plasma peptidomes formed discrete clusters that mostly coincided with the experimental groups. In addition, machine-learning algorithms discriminated the different experimental groups with a sensitivity of 95.7% and specificity of 90.9%. Data reveal the potential of plasma fingerprints analysis by MALDI-TOF-mass spectrometry as a simple, speedy and readily transferrable method for sepsis patient stratification that would contribute to therapeutic decision-making based on their immunological status.
由于众多临床试验的失败，败血症被称为制药公司的墓地。缺乏监测败血症免疫状态的工具限制了治疗方法的发展。在这里，我们评估了一种基于 MALDI-TOF 质谱仪获得的全血浆肽组和机器学习算法的测试，以区分两种模拟败血症期间可能存在的促炎/抗炎/免疫抑制环境的内毒素（LPS）诱导的小鼠模型。LPS 组接种了单高剂量 LPS，IS 组接受递增剂量 LPS，分别诱导促炎和抗炎/免疫抑制谱。LPS 组表现出白细胞减少症和更高水平的细胞因子和组织损伤标志物，而 IS 组表现出中性粒细胞增多、淋巴细胞减少和体液反应降低。血浆肽组的主成分分析形成了离散的聚类，这些聚类主要与实验组一致。此外，机器学习算法以 95.7%的灵敏度和 90.9%的特异性区分了不同的实验组。数据表明，MALDI-TOF 质谱仪的血浆指纹分析具有作为一种简单、快速且易于转移的败血症患者分层方法的潜力，这将有助于根据其免疫状态做出治疗决策。
