Department of Clinical Laboratory, The PLA General Hospital, Beijing, 100853, China.
Nankai University School of Medicine, Nankai University, Tianjin, 300071, China.
Proteomics Clin Appl. 2020 Mar;14(2):e1900075. doi: 10.1002/prca.201900075. Epub 2019 Dec 10.
Due to a lack of effective early diagnostic measures, new diagnostic methods for bacterial bloodstream infections (BSIs) are urgently needed. A protein-peptide profiling approach can be used to identify novel diagnostic biomarkers of BSIs.
In this study, MALDI-TOF MS and nano-LC/ESI-MS/MS are used to analyze serum peptides. In addition, GO and network analyses are conducted as a means of analyzing these potential protein markers. Finally, the potential biomarkers are verified in independent clinical samples via ELISA.
m/z 1533.8, 2794.3, 3597.3, 5007.3, and 7816.7 reveal an identical trend; the intensity of m/z 1533.8, 2794.3, and 3597.3 are higher in the infection group relative to controls, whereas the intensity of m/z 5007.3 and 7816.7 are lower in the infection group. Four peaks are successfully identified including ITIH4, KNG1, SAA2, and C3. GO and network analyses find these proteins to form an interaction network, which may be correlated with BSI. ELISA results indicate that ITIH4, KNG1, and SAA2 are effective in differentiating infected from normal control group and the febrile group.
These biomarkers have the potential to offer new insights into the signaling networks underlying the development and progression of BSI.
由于缺乏有效的早期诊断措施，迫切需要新的细菌性血流感染（BSI）诊断方法。蛋白质-肽谱分析方法可用于鉴定 BSI 的新型诊断生物标志物。
在这项研究中，MALDI-TOF MS 和纳升 LC/ESI-MS/MS 用于分析血清肽。此外，GO 和网络分析用于分析这些潜在的蛋白质标志物。最后，通过 ELISA 在独立的临床样本中验证潜在的生物标志物。
m/z 1533.8、2794.3、3597.3、5007.3 和 7816.7 呈现出相同的趋势；m/z 1533.8、2794.3 和 3597.3 的强度在感染组中高于对照组，而 m/z 5007.3 和 7816.7 的强度在感染组中较低。成功鉴定出 4 个峰，包括 ITIH4、KNG1、SAA2 和 C3。GO 和网络分析发现这些蛋白质形成相互作用网络，可能与 BSI 相关。ELISA 结果表明，ITIH4、KNG1 和 SAA2 可有效区分感染组与正常对照组和发热组。
这些生物标志物有可能为 BSI 的发展和进展提供新的信号网络见解。
