Deep data-independent acquisition-based plasma proteomic profiling unveils distinct molecular features in dengue fever with neutropenia

Guanyong Ou; Jun Wang; Rongrong Zou; Dongmei Lai; Qi Qian; Xiaowen Liang; Yuelin Wang; Canghai Ma; Hao Liao; Shiyu Niu; Jing Yuan; Yingxia Liu; Yang Yang

doi:10.1016/j.virs.2025.12.005

December 2025

Citation: Guanyong Ou, Jun Wang, Rongrong Zou, Dongmei Lai, Qi Qian, Xiaowen Liang, Yuelin Wang, Canghai Ma, Hao Liao, Shiyu Niu, Jing Yuan, Yingxia Liu, Yang Yang. Deep data-independent acquisition-based plasma proteomic profiling unveils distinct molecular features in dengue fever with neutropenia .VIROLOGICA SINICA, 2025, 40(6) : 884-897. http://dx.doi.org/10.1016/j.virs.2025.12.005

Deep data-independent acquisition-based plasma proteomic profiling unveils distinct molecular features in dengue fever with neutropenia

Guanyong Ou ^a ,
Jun Wang ^a,b ,
Rongrong Zou ^a,c ,
Dongmei Lai ^d ,
Qi Qian ^a,e ,
Xiaowen Liang ^a,f ,
Yuelin Wang ^a ,
Canghai Ma ^g ,
Hao Liao ^a ,
Shiyu Niu ^a ,
Jing Yuan ^{a,b,c
,,} ,
Yingxia Liu ^{a,b,c
,,} ,
Yang Yang ^{a,b,c
,,}

a. Shenzhen Key Laboratory of Pathogen and Immunity, Shenzhen Third People's Hospital, Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China;
b. Guangdong Key Laboratory for Diagnosis and Treatment of Emerging Infectious Diseases, Shenzhen 518112, China;
c. National Clinical Research Center for Infectious Disease, Shenzhen 518112, China;
d. School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong 518060, China;
e. School of Public Health, Guangdong Medical University, Dongguan, Guangdong 523808, China;
f. School of Pharmacy, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China;
g. College of Basic Medicine, Mudanjiang Medical University, Mudanjiang 157000, China

Corresponding author: Jing Yuan, 13500054798@139.com
Yingxia Liu, yingxialiu@hotmail.com
Yang Yang, young@mail.sustech.edu.cn
Received Date: 29 March 2025
Accepted Date: 04 December 2025
Available online: 08 December 2025

Abstract

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. In this study, the comprehensive plasma proteomic profiling of dengue fever (DF) patients, DF patients with neutropenia (DFN), and healthy controls (HC) was systematically analyzed using a deep data-independent acquisition (DIA) workflow combined with LC-MS/MS analysis, to elucidate key cellular pathways and identify promising biomarkers. 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 2475 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. In 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.
- Dengue fever (DF)
- , Neutropenia
- , Plasma proteomics
- , Data-independent acquisition (DIA)
- , Biomarkers
- , Machine learning

Electronic Supplementary Material

10.1016j.virs.2025.12.005.docx
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