Deep Data-independent Acquisition-based Plasma Proteomic Profiling Unveils Distinct Molecular Features in Dengue Fever with Neutropenia

Background: Dengue virus (DENV) remains a pervasive global health threat, further complicated by the occurrence of neutropenia—a distinct clinical feature indicative of an altered host immune response, closely correlated with progressive disease deterioration and increased severity. Nevertheless, the molecular mechanisms underlying dengue-associated neutropenia remain inadequately elucidated.Methods: Plasma samples were collected from dengue fever (DF) patients, DF patients with neutropenia (DFN), and healthy controls (HC). A comprehensive plasma proteomic profiling was conducted using a deep data-independent acquisition (DIA) workflow combined with LC-MS/MS analysis. Differential protein expression analysis and bioinformatic approaches, including functional enrichment analyses and machine learning, were utilized to elucidate key cellular pathways and identify promising biomarkers.Results: DFN patients exhibited significant dual hematological alterations, with notable changes in both platelet and neutrophil counts, reflecting a complex disturbance in hematological homeostasis during dengue progression. DIA analysis quantified 2,475 proteins, revealing widespread proteomic alterations among the DF, DFN, and HC subjects. Differential analysis highlighted significant fluctuations in proteins related to cytoskeletal organization, metabolic regulation, and intracellular signaling. Enrichment analyses implicated pathways such as focal adhesion, platelet activation, and PI3K-Akt signaling. Machine learning methods further identified a panel of four biomarkers—CNST, DSTN, DUSP3, and PDIA5—with high predictive accuracy for dengue diagnosis and subgroup differentiation.Conclusion: This study advances our understanding of dengue’s plasma proteomic landscape and underscores the synergistic potential of DIA-based proteomics and machine learning in unveiling host-response mechanisms, thereby informing early diagnosis and targeted therapeutic strategies.

搜集汇总

背景与挑战

背景概述

该数据集基于深度数据非依赖性采集（DIA）血浆蛋白质组学分析，揭示了登革热伴中性粒细胞减少症（DFN）患者的分子特征，包括量化2,475个蛋白质并发现与细胞骨架组织、代谢调节相关的显著波动。通过机器学习方法，识别出CNST、DSTN、DUSP3和PDIA5四个高预测准确性的生物标志物，有助于登革热的早期诊断和亚组区分，为理解宿主免疫反应和开发靶向治疗策略提供关键见解。