Quantitative N-glycoproteomics reveals altered glycosylation levels of various plasma proteins in bloodstream infected patients.
Quantitative N-glycoproteomics reveals altered glycosylation levels of various plasma proteins in bloodstream infected patients.
复制标题
DOI：
10.1371/journal.pone.0195006
复制
发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Renkonen R
中科院分区：
文献类型：
--
作者：
Joenvaara S;Saraswat M;Kuusela P;Saraswat S;Agarwal R;Kaartinen J;Järvinen A;Renkonen R
血液感染与高发病率和高死亡率有关，发病率从10-25%不等，甚至更高。适当和及时的抗生素治疗影响着这些患者的预后。它需要诊断的准确性，这是目前的黄金标准，如血液培养无法提供的。此外，从采血到血培养结果的时间是降低死亡率的关键决定因素。目前还没有已建立的生物标志物可以将血流感染与其他全身炎症情况区分开来。这需要研究血浆分子图谱的生物标志物潜力，因为它最受伴随血流感染的分子变化的影响。N-糖基化是一种翻译后修饰，对生理变化非常敏感。在这里，我们对血培养阳性、年龄和性别匹配、血培养阴性的发热对照患者的血浆样本进行了有针对性的定量N-糖蛋白组学研究。用质谱法对368个潜在的N-糖肽进行了定量，并进一步选择了14 9个进行鉴定。共鉴定了24个N-糖肽，并对其序列、N-糖基化位点、糖链组成和结构进行了分析。采用主成分分析、潜在结构正交投影判别分析(S图)和自组织映射聚类等统计方法对数据进行分析。这些方法使我们清楚地区分了两个患者类别。我们提出了高置信度N-糖肽，它具有从血培养阴性发热患者中分离血流感染的能力，并揭示了菌血症期间的宿主反应。数据可通过标识符为PXD009048的ProteomeXchange获得。
Bloodstream infections are associated with high morbidity and mortality with rates varying from 10–25% and higher. Appropriate and timely onset of antibiotic therapy influences the prognosis of these patients. It requires the diagnostic accuracy which is not afforded by current gold standards such as blood culture. Moreover, the time from blood sampling to blood culture results is a key determinant of reducing mortality. No established biomarkers exist which can differentiate bloodstream infections from other systemic inflammatory conditions. This calls for studies on biomarkers potential of molecular profiling of plasma as it is affected most by the molecular changes accompanying bloodstream infections. N-glycosylation is a post-translational modification which is very sensitive to changes in physiology. Here we have performed targeted quantitative N-glycoproteomics from plasma samples of patients with confirmed positive blood culture together with age and sex matched febrile controls with negative blood culture reports. Three hundred and sixty eight potential N-glycopeptides were quantified by mass spectrometry and 149 were further selected for identification. Twenty four N-glycopeptides were identified with high confidence together with elucidation of the peptide sequence, N-glycosylation site, glycan composition and proposed glycan structures. Principal component analysis, orthogonal projections to latent structures-discriminant analysis (S-Plot) and self-organizing maps clustering among other statistical methods were employed to analyze the data. These methods gave us clear separation of the two patient classes. We propose high-confidence N-glycopeptides which have the power to separate the bloodstream infections from blood culture negative febrile patients and shed light on host response during bacteremia. Data are available via ProteomeXchange with identifier PXD009048.
登录
查看更多内容
影响因子：
30.3
作者：
Siegel SJ;Roche AM;Weiser JN
通讯作者：
Weiser JN
影响因子：
11.1
作者：
Nikfardjam, M;Müllner, M;Huber, K
通讯作者：
Huber, K
影响因子：
11.8
作者：
Jenkins, Timothy C.;Price, Connie S.;Burman, William J.
通讯作者：
Burman, William J.
影响因子：
4.4
作者：
Saraswat, Mayank;Joenvaara, Sakari;Renkonen, Risto
通讯作者：
Renkonen, Risto
影响因子：
3.1
作者：
Fumagalli, O;Tall, BD;Oelschlaeger, TA
通讯作者：
Oelschlaeger, TA