Serum Protein Changes in Pediatric Sepsis Patients Identified With an Aptamer-Based Multiplexed Proteomic Approach

败血症 医学 全身炎症反应综合征 队列 器官功能障碍 重症监护医学 免疫学 内科学 生物信息学 生物
作者
Nicholas J. Shubin,Krupa Navalkar,Dayle Sampson,Thomas D. Yager,Silvia Cermelli,Therese Seldon,Erin Sullivan,Jerry J. Zimmerman,Lester C. Permut,Adrian M. Piliponsky
出处
期刊:Critical Care Medicine [Lippincott Williams & Wilkins]
卷期号:48 (1): e48-e57 被引量:6
标识
DOI:10.1097/ccm.0000000000004083
摘要

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a leading cause of death and disability among children worldwide. Identifying sepsis in pediatric patients is difficult and can lead to treatment delay. Our objective was to assess the host proteomic response to infection utilizing an aptamer-based multiplexed proteomics approach to identify novel serum protein changes that might help distinguish between pediatric sepsis and infection-negative systemic inflammation and hence can potentially improve sensitivity and specificity of the diagnosis of sepsis over current clinical criteria approaches.Retrospective, observational cohort study.PICU and cardiac ICU, Seattle Children's Hospital, Seattle, WA.A cohort of 40 children with clinically overt sepsis and 30 children immediately postcardiopulmonary bypass surgery (infection-negative systemic inflammation control subjects) was recruited. Children with sepsis had a confirmed or suspected infection, two or more systemic inflammatory response syndrome criteria, and at least cardiovascular and/or pulmonary organ dysfunction.None.Serum samples from 35 of the sepsis and 28 of the bypass surgery subjects were available for screening with an aptamer-based proteomic platform that measures 1,305 proteins to search for large-scale serum protein expression pattern changes in sepsis. A total of 111 proteins were significantly differentially expressed between the sepsis and control groups, using the linear models for microarray data (linear modeling) and Boruta (decision trees) R packages, with 55 being previously identified in sepsis patients. Weighted gene correlation network analysis helped identify 76 proteins that correlated highly with clinical sepsis traits, 27 of which had not been previously reported in sepsis.The serum protein changes identified with the aptamer-based multiplexed proteomics approach used in this study can be useful to distinguish between sepsis and noninfectious systemic inflammation.
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