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研究生:吳永富
研究生(外文):WU, YUNG-FU
論文名稱:探討人類水通道Aquaporin 4的基因多型性,對思覺失調症受試者疾病病程及臨床藥物治療療效的影響-以一個南方漢民族為例
論文名稱(外文):Exploring the Impact of Human Aquaporin 4 Gene Polymorphisms on Disease Progression and Treatment Response of Schizophrenia in a Southern Chinese Han Population
指導教授:龍佛衛龍佛衛引用關係司徒惠康司徒惠康引用關係
指導教授(外文):LUNG, FOR-WEYSYTWU, HUEY-KANG
口試委員:龍佛衛司徒惠康黃坤崙顏永杰李明濱
口試委員(外文):LUNG, FOR-WEYSYTWU, HUEY-KANGHUANG, KUN-LUNYEN, YUNG-CHIEHLEE, MING-BEEN
口試日期:2020-10-30
學位類別:博士
校院名稱:國防醫學院
系所名稱:醫學科學研究所
學門:醫藥衛生學門
學類:醫學學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:英文
論文頁數:92
中文關鍵詞:思覺失調症AQP4水通道單核甘酸多型性S100B蛋白單倍型星型膠細胞
外文關鍵詞:schizophrenia (SCZ)aquaporin 4 (AQP4)single nucleotide polymorphism (SNP)haplotypeS100 calcium-binding protein B (S100B)astrocyte
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背景:近年來,多方證據顯示精神疾病的發生,可能與腦部發生發炎反應相關。其中,腦組織中,由星形膠細胞、動、靜脈及淋巴組織所組成的大腦血液屏障,負責帶動物質從周邊血管到中樞的流動。其中,位處於星形膠細胞終足上的AQP4水通道被認為是負責調控大腦血液屏障的通透性及影響精神疾病形成的重要關鍵。然而,在思覺失調症中這方面的研究有限。過往我們的研究顯示AQP4基因多型性與臨床抗精神病藥物劑量相關。因此,AQP4基因多型性的角色在大腦血液腦屏障和不同的神經保護作用需要進一步的研究。我們研究目的在了解思覺失調症受試者及健康受試者,兩者在AQP4基因多型性的差異。以及,AQP4基因多型性對於思覺失調症受試者,在藥物治療下的臨床症狀,及血中S100B數值變化的影響。
方法:在第一個研究中,我們招募了190位思覺失調症受試者,這些受試者提供了人口統計學的相關資料及。同時,我們採集受試者血液樣本來進行3種AQP4單核苷酸多型性(SNP)和8種單倍型(haplotype)分析。在抗精神病藥物治療期間,我們分別在收案時、收案後3週、6週及9週,進行正性與負性症狀量表(PANSS),個人與社會功能量表(PSP),整體功能性評估(GAF),臨床整體評估表(CGI)的評分,並重複4次測量血清S100B濃度。使用廣義估計方程式(GEE)分析,探討重複4次測量的對數轉換後的S100B (logS100B)數值、AQP4基因多型性和量表分數的關聯性。在第二個研究中,我們收集了292名思覺失調症受試者和100名健康受試者。為了研究AQP4基因多型性和發生思覺失調症風險的關係,我們將100名健康對照組和100名配對後罹患思覺失調症的某一南方漢民族受試者進行分析。使用卡方檢測來比較對照組和疾病組之間的等位基因和基因型分佈。
結果:經由觀察190位思覺失調症的血液數值常態分布狀況,我們認為logS100B數值1.78具有最佳的鑑別性,並將受試者分為兩組。進而發現9週藥物治療後,血清S100B數值會逐漸降低。AQP4基因多型性中的TAA單倍型與血清S100B數值升高具相關性(p = 0.006)。PANSS負性分項(PANSS-N)(p = 0.001)和臨床整體評估表-改善(CGI-1)(p = 0.003)數值與S100B數值呈現正相關。在第二個研究中,我們選取了三個相同的單核甘酸多型性。與健康對照組相比,受試者在rs1058424處的T等位基因出現頻率較高,在rs3763043處的G等位基因出現頻率較高(分別為p = 0.043和p = 0.045)。此外,rs1058424(p = 0.021,OR = 2.04)和rs3763043(p = 0.018,OR = 2.25)帶有AA基因型時,出現罹患思覺失調症的風險降低。TCG單倍型(p = 0.036)罹患思覺失調症的風險較高,而ACA單倍型(p = 0.0007)與罹患思覺失調症的風險較低,並且ACA單倍型在經過Bonferroni校正後仍具有統計學意義(p = 0.006)。
結論:AQP4的基因多型性可能與罹患思覺失調症的風險相關。藥物治療下,帶有AQP4基因多型性中的TAA單倍型的受試者,可能具有較高的血清S100B數值、較高的負性分項數值及控制不良的神經發炎反應。對數S100B數值1.78可能可以做為預測受試者負性分項嚴重程度的的指標。
Background: Multiple lines of evidence support the pathogenic role of neuroinflammation in psychiatric illness. The blood-brain barrier (BBB) with the “glymphatic” drainage formation in the central nervous system (CNS) through perivascular processes involves astrocytes. The aquaporin 4 (AQP4) gene, which is found primarily at the end-feet of astrocytes, has been suspected to play putative roles in the clearance of the glymphatic system as well as the development of psychiatric disorders. However, the limited research on AQP4 leaves many uncertainties of the gene. The influence the AQP4 polymorphism may have on the required dosage of antipsychotic drugs has been shown in previous research. We aim to determine whether an association remains between the gene polymorphisms and haplotype of AQP4, and clinical symptoms in patients with schizophrenia (SCZ) and serum S100 calcium-binding protein B (S100B). in the Southern Chinese Han population.
Methods: 190 patients with SCZ were recruited in the first study. These candidates submitted samples to test for four AQP4 tag single nucleotide polymorphisms (SNPs) along with eight haplotypes, provided demographic data and also completed relevant questionnaires. We repeatedly measured their serum S100B levels during antipsychotic treatment at weeks 0 (baseline), 3, 6, and 9, and also assessed rating scales of Positive and Negative Syndrome Scale (PANSS), Personal and Social Performance (PSP), the Global Assessment of Functioning (GAF), Clinical Global Impression (CGI). Log-transformed S100B (logS100B) level was tested for associations with haplotype and other dependent variables using generalized estimating equation (GEE) analyses. We enrolled 100 healthy individuals and 292 patients in the second study. Genetic information was drawn from a cohort of 100 patients with SCZ and 100 matched healthy controls of Southern Han Chinese descent to investigate the relationship between SCZ and APQ4 gene polymorphisms. We used the X2 test to make comparisons of the genotype and allele distributions between case and control groups. Assessments of the haplotype and the linkage equilibrium were made with two-group comparisons.
Results: The one that had the best discriminant validity for stratifying patients into two groups was logS100B level >1.78 or ≤1.78 when doing discretization via the median split procedure. Serum S100B level decreased after 9 weeks of treatment. There is an association between increased serum S100B level (p = 0.006) and the TAA haplotype of AQP4 SNPs. A positive association between S100B level and scores of the Clinical Global Impression-Improvement (CGI-I) (p = 0.003) and the PANSS negative subscale (PANSS-N) (p = 0.001). Three SNPs were found in the second study. Patients had higher G-allele frequencies at rs3763043 and T-allele frequencies at rs1058424 (p = 0.045 and p = 0.043, respectively) when compared to healthy controls. In addition, the AA genotype at both rs1058424 (p = 0.021, OR = 2.04) and rs3763043 (p = 0.018, OR = 2.25) are associated with decreased risk of SCZ. Potential risk of SCZ was found to be associated with the TCG haplotype (p = 0.036) while the ACA haplotype (p = 0.0007) associated with decreased risk of SCZ, after Bonferroni correction, retained its statistical significance (p = 0.006).
Conclusion: Patients are more likely to have negative symptoms, poor control of neuroinflammation and increased serum S100B level if they have the TAA haplotype of the APQ4 polymorphism. A logS100B level >1.78 may be sufficiently specific to predict a higher severity of negative symptoms. Association between risk of SCZ and AQP4 gene polymorphisms was shown in the second study which included patients and matched healthy controls.
Index of Contents ........................................................................................................I
List of Tables .............................................................................................................IV
List of Figures .............................................................................................................V
Chinese Abstract .......................................................................................................VI
English Abstract .....................................................................................................VIII
List of abbreviations ..............................................................................................XIII
1. Introduction .............................................................................................................1
1.1. Background ............................................................................................................1
1.1.1. The haploview concept………………………………………………………….3
1.2. The neuroinflammation hypothesis of psychiatric disorders .................................5
1.2.1. The role of blood brain barrier ............................................................................5
1.2.2. Neuroinflammation .............................................................................................6
1.3. The association of S100B, neuroinflammation and AQP4 ....................................6
1.3.1. The introduction of AQP4 ...................................................................................6
1.3.2. The noncoding regions of AQP4 gene ................................................................7
1.3.3. The introduction of S100 and S100B protein.......................................................7
1.3.4 Association between genetic variants and treatment response to antipsychotics10
1.4. The aims ...............................................................................................................11
2. Materials and Methods .........................................................................................12
2.1. Participants............................................................................................................12
2.1.1. Patients with SCZ ..............................................................................................13
2.1.2. Healthy controls ................................................................................................14
2.1.3. Demographic variables ......................................................................................15
2.2. The Psychometric Assessment .............................................................................15
2.2.1. PANSS ..............................................................................................................15
2.2.2. PSP ....................................................................................................................16
2.2.3. GAF ...................................................................................................................17
2.2.4. CGI ....................................................................................................................17
2.2.4.1 Severity scale ...................................................................................................17
2.2.4.2 Improvement scale……...................................................................................18
2.3. Blood sampling for genotyping ............................................................................19
2.3.1 Segment selection and primer design…………………………………………..19
2.3.2. Polymerase chain reaction (PCR) amplification………………………………20
2.3.3 DNA sequencing, SNP selection, and genotyping……………………………..20
2.3.4 Haplotype reconstruction…………………………………...………………….21
2.3.5 Human S100B ELISA detection ........................................................................21
2.4. Statistical Analyses ..............................................................................................22
2.4.1. Data processing and statistical analyses ............................................................23
3. Results…………………………………………………………………………….24
3.1. Demographic data ................................................................................................24
3.2. Repeated measurements of psychometric assessment and serum S100B level…25
3.3. Single marker analysis for AQP4 variants and SCZ…………………………….26
3.4. Linkage disequilibrium and Haplotype analysis for AQP4 variants and SCZ......26
3.5. Association between AQP4 variants, S100B and the psychometric assessment..27
4. Discussion…………………………………………………………………………28
4.1. The association S100B level and neuroinflammation in SCZ .............................28
4.1.1 The association of S100B level and the assessment ..........................................30
4.1.2 The role of AQP4 variants to affect S100B level and neuroinflammation in patients with SCZ…………………………………………………………….....31
4.2 The association of AQP4 gene and neuroinflammation in case-control study.....32
4.2.1 Polymorphisms of AQP4 genes in neuropsychiatry disease ..............................34
4.2.2 Polymorphisms of AQP4 genes and SCZ vulnerability..................................... 35
4.3 Limitations.............................................................................................................36
5. Conclusion………………………………………………………………………...38
References ..................................................................................................................40
Tables ..........................................................................................................................49
Figures ........................................................................................................................52
Appendix ....................................................................................................................67

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