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研究生： 傅立宇
Irwin Ivandi Puc
論文名稱： 縱向分析登革病毒感染後之細胞激素水平及造血幹細胞動員
Longitudinal analysis of cytokine and hematopoietic stem cells mobilization profiling in subsequent dengue infection
指導教授： 陳柏齡
Chen, Po-Lin
學位類別： 博士
Doctor
系所名稱： 醫學院 - 基礎醫學研究所
Institute of Basic Medical Sciences
論文出版年： 2023
畢業學年度： 111
語文別： 英文
論文頁數： 117
中文關鍵詞： 登革病毒 、疾病嚴重程度 、造血幹細胞動員 、生物標誌 、CD44 受體
外文關鍵詞： Dengue virus, Disease severity, HSCs mobilization, Biomarker, CD44 receptor
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    • 登革熱的臨床症狀具多樣性且不具特異性，導致疾病進展和結果難以預測。因此，藉由分析患者初次就醫時體內的細胞激素表現或許可以預測患者是否容易發展為重症登革熱。因此，在本研究中，我們檢測了23種細胞激素的變化，包括介白素(IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-17A, IL-33)、細胞表面分子分化群(CD14, CD54, CD62E, CD62L, CD62p, CD106, CD121b, CD154, CD178)、顆粒球及單核球群落刺激生長因子(GM-CSF)、干擾素-γ (IFN-g)、巨噬細胞移動抑制因子 (MIF)、生長刺激表達基因2蛋白 (ST2)和腫瘤壞死因子 (TNF)等，探討可能用於預測登革熱疾病嚴重程度的生物標誌以利提供相關治療。在本研究中，介白素-10 (IL-10)被證明是登革熱潛在的診斷標誌 (分析控制組和登革熱患者之AUC = 0.944；分析控制組與非登革病毒引起之發燒患者之AUC = 0.969)。介白素-1第II型受體 (CD121b) 則被證明能夠預測登革熱的疾病嚴重程度 (分析登革患者有無警示徵象之AUC = 0.744；分析登革患者具警示徵象及重症登革患者之AUC = 0.775)。此外，我們也研究了登革熱患者在登革病毒感染期間體內的造血幹細胞及前驅細胞動員的情形。在正常情況下，造血幹細胞及前驅細胞會保留在骨髓內特化的利基微環境(niche)中，但在病原感染或有細胞壓力的情況下，造血幹細胞及前驅細胞則會被保護性的動員。從我們的結果顯示，隨著患者從登革輕症發展至更嚴重的登革疾病時，造血前驅細胞的動員及其歸巢 (homing)能力會下降。此外，我們發現造血前驅細胞不但容易受到登革病毒感染，並且在培養後還可以再產生具傳染能力的登革病毒。因此，我們認為造血幹細胞族群可能是登革病毒感染的首要目標並且對於後續的感染是至關重要的。我們也透過蛋白質體學的方式分析造血幹細胞可能的登革病毒受體，結果顯示 CD44分子可能在幹細胞被登革病毒感染時作為受體的角色。我們也利用CD44分子的抗體及玻尿酸配體以阻斷 CD44分子 ，結果發現在阻斷CD44分子之後登革病毒感染 的滴度有顯著降低的現象。
    從我們的研究中更加了解到在登革病毒感染的期間造血前驅細胞的重要性，有利於未來為登革熱患者制定良好的照護策略。此外，我們也提供了嶄新的觀點，認為介白素-10 (IL-10) 和介白素-1第II型受體 (CD121b) 可以分別作為診斷登革熱和預測登革熱疾病嚴重程度的生物標誌。

    Clinical presentations of dengue are diverse and non-specific, causing unpredictable progression and outcomes. Analyzing cytokine levels upon presentation can offer insights into whether a patient is prone to developing severe manifestations of dengue or not. Therefore, in this study, we first examined the signature profile change of 23 cytokines namely, IL-1b, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12p70, IL-17A, IL-33, CD14, CD54, CD62E, CD62L, CD62p, CD106, CD121b, CD154, CD178, GM-CSF, IFN-g, MIF, ST2 and TNF simultaneously with the intent to find possible biomarkers for effective dengue severity prognosis and intervention strategies. IL-10 demonstrated to be a potential diagnostic marker for dengue fever (Healthy and DENV+ group; AUC = 0.944, Healthy and OF group; AUC = 0.969). CD121b demonstrated to be predictive of the disease severity (DWoWS and DWWS group; AUC = 0.744, DWWS and SD group; AUC = 0.775). Furthermore, we investigated the hematopoietic stem and progenitor cells (HSPCs) mobilization in dengue patients during dengue virus (DENV). Under normal conditions, hematopoietic stem and progenitor cells (HSPCs) remain in specialized niches within the bone marrow but mobilize during an infection or stress condition as a protective response. Results demonstrated that the mobilization of HSPCs and their homing capacity decreased as the patients proceeded from mild dengue to a more severe form. Additionally, we showed that mobilizing HSPCs were not only permissive to DENV infection, but infectious DENV could be recovered after coculture. Finally, we showed that HSCs population were crucial for DENV infection and possibly the primary target for the virus. Identification of possible DENV receptors on HSCs population by proteomics revealed CD44 as putative DENV receptor in stem cells. Blocking of CD44 by antibody or ligand Hyuranic acid demonstrated a significant decrease in viral titer upon infection with DENV.
    Our findings underscore the importance of investigating HSPCs and its role during DENV to develop appropriate countermeasures. More importantly, we provide a new insight into IL-10 and CD121b as biomarkers for diagnosing dengue fever and predicting dengue disease severity stage, respectively.

    中文摘要 I Abstract III Acknowledgment V Contents VII List of Figures XII List of Tables XIV Abbreviation XV Chapter 1 Introduction 1 1. Dengue epidemiology 1 1.1 Dengue importance, distribution, and global burden 2 1.2 Dengue transmission 3 2. Dengue virus 4 2.1 Structure 5 2.2 Genetic diversity 6 3. Dengue pathogenesis 6 3.1 Implantation of virus and entry 7 3.2 Antibody-dependent enhancement (ADE) 8 3.3 Cytokine storm (Hypercytokinemia) 8 3.4 Cytokine as a biomarker 10 4. Dengue classification, clinical manifestation, and challenges 11 4.1 Dengue classification and manifestation 11 4.2 Bone marrow suppression and hematopoietic stem and progenitor cells (HSPCs) mobilization 12 4.3 Dengue management challenges 13 Chapter 2 Experimental designs 16 Chapter 3 Materials and methods 17 3.1 Materials 17 3.1.1 Cell lines and virus 17 3.1.1b Viruses 17 3.1.2 Antibodies 17 3.1.2a Flow cytometry 17 3.1.2b VOPBA antibodies 18 3.1.2c Magnetic beads 18 3.1.3 Reagents 19 3.1.4 Cell culture medium and buffers ingredients 21 3.1.4a Cell culture medium 21 3.1.4b Plaque assay medium 22 3.1.4c Flow cytometry buffers 22 3.1.4d Co-Immunoprecipitation buffers 23 3.1.4e VOPBA buffers 23 3.1.5 Equipments 24 3.1.6 Software 25 3.1.7 Databases 26 3.1.8 Algorithms 26 3.2 Methods 27 3.2.1 Study cohort and ethical statement 27 3.2.2 Dengue confirmatory test 28 3.2.3 Cytokine identification and quantification 28 3.2.4 White blood cells preparation 29 3.2.5 Flow cytometric analysis 29 3.2.6 Gating strategy for HSPCs, homing HSPCs, and DENV-infected or uninfected HSPCs 30 3.2.7 Fluorochrome conjugated DENV NS1 antibody 32 3.2.8 HSPCs isolation by magnetic bead isolation 32 3.2.9 Vital titration 33 3.2.10 Cell Culture (BHK-21, Vero cells, K-562 and HaCAT) 34 3.2.11 Cell membrane preparation and viral overlay protein binding assay (VOPBA) 34 3.2.13 Ultraviolet (UV) crosslinking and Co-immunoprecipitation (Co-IP) for protein identification 35 3.2.14 Decision tree algorithm 36 3.2.15 Statistical Analysis 36 Chapter 4 Results 37 4.1 Longitudinal cytokine profiling in associations with the disease 37 4.1.1 Hypercytokinaemia in dengue patients 37 4.1.2 Cytokine profiles distinguish between moderate and severe disease. 38 4.1.3 Perturbed cytokine profiles and signs of endothelial dysfunction at days of infection 39 4.2 Development and validation of the cytokine signature for diagnosis of dengue fever and discriminating between the disease severity 40 4.2.1 Identification of IL-10 as a potential diagnostic biomarker for dengue fever. 40 4.2.2 CD121b as a potential diagnostic biomarker for distinguishing the disease severity 41 4.2.3 Correlation of IL-10 and CD121b levels between age and DENV titer 41 4.2.4 IL-10 and CD121b performance and classification accuracy in discriminating Dengue fever, and dengue disease severity using rpart and CART algorithms 42 4.3 Mobilization of hematopoietic stem and progenitor cells (HSPCs) during DENV infection 43 4.3.1 Circulation of HSPCs decreased as the disease severity progressed 43 4.3.2 The mobilization capacity of HSPCs decreased as the disease severity progressed 44 4.3.3 HSPCs in dengue patients were permissive for DENV Infection 45 4.4 The importance of hematopoietic stem cells (HSCs) in dengue virus infection 46 4.4.1 HSCs were essential for DENV Infection in BM 47 4.4.2 Validating the importance of HSCs for DENV infection using cell line 48 4.5 Identification of possible DENV entry receptors on hematopoietic stem cells (HSCs) 48 4.5.1 CD9, CD63, CD44 and CD151 are potential entry receptors 49 4.5.2 Blocking of CD44 showed a decrease in viral titer upon infection with DENV infection 50 Chapter 5 Discussion 52 Longitudinal cytokine profiling in associations with the disease and biomarker identification 52 Mobilization of hematopoietic stem and progenitor cells (HSPCs) during DENV infection 57 The importance of hematopoietic stem cells (HSCs) in dengue virus infection 59 Chapter 6 Conclusion 63 Chapter 7 References 64 Chapter 8 Figures and tables 75 Main figures 75 Supplementary figures 98 Supplementary Tables 104 Chapter 9 Appendix 107 Chapter 10 Curriculum Vitae 115

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