小儿呼吸道病毒感染的局部和全身免疫反应的蛋白质组学分析。

IF 5 2区 生物学 Q1 MICROBIOLOGY
mSystems Pub Date : 2025-01-21 Epub Date: 2024-11-29 DOI:10.1128/msystems.01335-24
Emily Lydon, Christina M Osborne, Brandie D Wagner, Lilliam Ambroggio, J Kirk Harris, Ron Reeder, Todd C Carpenter, Aline B Maddux, Matthew K Leroue, Nadir Yehya, Joseph L DeRisi, Mark W Hall, Athena F Zuppa, Joseph Carcillo, Kathleen Meert, Anil Sapru, Murray M Pollack, Patrick McQuillen, Daniel A Notterman, Charles R Langelier, Peter M Mourani
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引用次数: 0

摘要

病毒性下呼吸道感染(vLRTI)是全球儿童住院和死亡的主要原因。尽管如此,还没有研究使用蛋白质组学来表征严重儿童下气道和体循环vLRTI的宿主免疫反应。为了解决这一空白,深入了解vLRTI病理生理学,并测试一种新的诊断方法,我们使用SomaScan检测了62名危重儿童的气管吸入物(TA)和血浆中的1305种蛋白质。我们将vLRTI患者(n = 40)与非传染性急性呼吸衰竭患者(n = 22)进行了差异表达(DE)和途径分析,使用LASSO回归开发了诊断分类器,并分析了匹配的TA和血浆样本。我们进一步研究了病毒载量和细菌共感染对蛋白质组的影响。用200个DE蛋白(Padj)表征了vLRTI的TA特征
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proteomic profiling of the local and systemic immune response to pediatric respiratory viral infections.

Viral lower respiratory tract infection (vLRTI) is a leading cause of hospitalization and death in children worldwide. Despite this, no studies have employed proteomics to characterize host immune responses to severe pediatric vLRTI in both the lower airway and systemic circulation. To address this gap, gain insights into vLRTI pathophysiology, and test a novel diagnostic approach, we assayed 1,305 proteins in tracheal aspirate (TA) and plasma from 62 critically ill children using SomaScan. We performed differential expression (DE) and pathway analyses comparing vLRTI (n = 40) to controls with non-infectious acute respiratory failure (n = 22), developed a diagnostic classifier using LASSO regression, and analyzed matched TA and plasma samples. We further investigated the impact of viral load and bacterial coinfection on the proteome. The TA signature of vLRTI was characterized by 200 DE proteins (Padj <0.05) with upregulation of interferons and T cell responses and downregulation of inflammation-modulating proteins including FABP and MIP-5. A nine-protein TA classifier achieved an area under the receiver operator curve (AUC) of 0.96 (95% CI: 0.90-1.00) for identifying vLRTI. In plasma, the host response to vLRTI was more muted with 56 DE proteins. Correlation between TA and plasma was limited, although ISG15 was elevated in both compartments. In bacterial coinfection, we observed increases in the TNF-stimulated protein TSG-6, as well as CRP, and interferon-related proteins. Viral load correlated positively with interferon signaling and negatively with neutrophil-activation pathways. Taken together, our study provides fresh insights into the lower airway and systemic proteome of severe pediatric vLRTI and identifies novel protein biomarkers with diagnostic potential.IMPORTANCEWe describe the first proteomic profiling of the lower airway and blood in critically ill children with severe viral lower respiratory tract infection (vLRTI). From tracheal aspirate (TA), we defined a proteomic signature of vLRTI characterized by increased expression of interferon signaling proteins and decreased expression of proteins involved in immune modulation including FABP and MIP-5. Using machine learning, we developed a parsimonious diagnostic classifier that distinguished vLRTI from non-infectious respiratory failure with high accuracy. Comparative analysis of paired TA and plasma specimens demonstrated limited concordance, although the interferon-stimulated protein ISG15 was significantly upregulated with vLRTI in both compartments. We further identified TSG-6 and CRP as airway biomarkers of bacterial-viral coinfection, and viral load analyses demonstrated a positive correlation with interferon-related protein expression and a negative correlation with the expression of neutrophil activation proteins. Taken together, our study provides new insights into the lower airway and systemic proteome of severe pediatric vLRTI.

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来源期刊
mSystems
mSystems Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
10.50
自引率
3.10%
发文量
308
审稿时长
13 weeks
期刊介绍: mSystems™ will publish preeminent work that stems from applying technologies for high-throughput analyses to achieve insights into the metabolic and regulatory systems at the scale of both the single cell and microbial communities. The scope of mSystems™ encompasses all important biological and biochemical findings drawn from analyses of large data sets, as well as new computational approaches for deriving these insights. mSystems™ will welcome submissions from researchers who focus on the microbiome, genomics, metagenomics, transcriptomics, metabolomics, proteomics, glycomics, bioinformatics, and computational microbiology. mSystems™ will provide streamlined decisions, while carrying on ASM''s tradition of rigorous peer review.
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