臺灣博碩士論文加值系統

核糖核酸酶-L(ribonuclease L, RNase L)與其上游寡腺苷酸合成酶(oligoadenylate synthetase, OAS)、下游視黃酸誘發基因1(retinoic acid-inducible gene 1, RIG-I)等蛋白，負責核糖核酸型病毒入侵的防禦工作。近年來，科學家發現核糖核酸酶-L能專一降解特定信息核糖核酸(messenger RNA, mRNA)來調控如肌肉生成、細胞生長或致癌作用等功能。於此，本實驗室欲探討核糖核酸酶-L在脂肪新生(adipogenesis)所涉及的可能調控機制，乃至於與代謝症候群的關聯性。
本研究發現在3T3-L1前脂肪細胞(pre-adipocyte)中削弱核糖核酸酶-L的基因表現，會抑制脂肪細胞分化並影響其油滴聚集。於此同時，一個早期脂肪新生的抑制因子-脂肪前驅細胞因子-1(pre-adipocyte factor-1, Pref-1)之信息核糖核酸(messenger RNA, mRNA)表現量上升，其下游的焦點附著激酶(focal adhesion kinase, FAK)、細胞外訊息調節蛋白酶(extracellular signal-regulated kinases, ERK)與Y染色體性別決定區9號蛋白(sex determining region Y-box 9, Sox9)均隨著脂肪前驅細胞因子-1的上升而增加活性或蛋白量。因此提出一個假說：核糖核酸酶-L能夠降解脂肪前驅細胞因子-1的信息核糖核酸，並能藉此調控脂肪新生。使用免疫沉澱法(immunoprecipitation, IP)藉由專一抗體將核糖核酸酶-L蛋白與其信息核糖核酸複合體捕捉下來，並以反轉錄聚合酶連鎖反應(RT-PCR)方式，偵測到脂肪前驅細胞因子-1之信息核糖核酸分子於此複合體中出現。同時，本研究也觀察到脂肪前驅細胞因子-1信息核糖核酸之降解速率於過量表現核糖核酸酶-L的3T3-L1前脂肪細胞中，相較於失去活性的組別要快。此外，若在穩定敲落核糖核酸酶-L基因表現的3T3-L1前脂肪細胞中，同時藉由小分子核醣核酸干擾技術抑制脂肪前驅細胞因子-1表現，將能顯著地回復脂肪細胞分化與油滴聚集能力。
除體外培養細胞實驗外，核糖核酸酶-L與脂肪前驅細胞因子-1基因表現的關聯性，也藉由利用公開的核糖核酸表現微陣列晶片數據進行整合分析來進一步驗證。本研究使用統計軟體R的”多微陣列穩健算法(robust multi-array average, RMA)”來標準化微陣列數據。從五十六組小鼠脂肪組織微陣列整合分析中顯示，核糖核酸酶-L與脂肪前驅細胞因子-1的信息核糖核酸量呈現負相關。此外，於脂肪細胞發育早期的小鼠胚胎(embryos)、小鼠胚胎纖維母細胞(mouse embryonic fibroblasts, MEFs)之整合分析當中，此負相關亦存在。此外，於此整合分析與大鼠動物實驗更發現高脂飼料的餵食，能誘導囓齒類動物其脂肪組織表現比較高的核糖核酸酶-L與較低的脂肪前驅細胞因子-1。
數十年來，全世界代謝症候群(metabolic syndrome, MetS)的盛行率不斷攀升。既然上述的動物與細胞實驗中發現核糖核酸酶-L與脂肪新生與肥胖有相關，思考人體中是否亦有此關聯性，自然成為一重要課題。以本實驗室自行建構出來的酵素免疫分析法(enzyme-linked immunosorbent assay, ELISA)，能夠運用於測量人類血清中核糖核酸酶-L蛋白量。本研究發現於三百九十六位自願受試者中，代謝症候群實驗群組血清中的核糖核酸酶-L濃度相較於非代謝症候群群組較低(平均值分別為16.5±6.4 μg/ml與18.4±8.0 μg/ml，P=0.018)。其中，有中央型肥胖(central obesity)、血壓偏高(elevated blood pressure)與空腹葡萄糖耐受不良(impaired fasting glucose, IFG)者，相對於各項正常者血清有較低的核糖核酸酶-L濃度。於多變量變項線性回歸分析(multivariate linear regression analysis)當中，舒張壓(diastolic blood pressure) (β值為-0.124，P=0.031)及高密度脂蛋白膽固醇(high-density lipoprotein cholesterol, HDL-C) (β值為0.131，P=0.038)與核糖核酸酶-L血清濃度有顯著相關。每增加5 μg/ml核糖核酸酶-L血清濃度，將減少罹患代謝症候群(勝算比為0.83，95%信賴區間為0.71-0.98，P=0.028)、中央型肥胖(勝算比為0.82，95%信賴區間為0.71-0.94，P=0.005)、高密度脂蛋白膽固醇低下(勝算比為0.86，95%信賴區間為0.74-1.00，P=0.042)的風險。除此之外，在不同的統計分析當中，受試者年齡也持續地與血清核糖核酸酶-L濃度呈現負相關。
綜合以上結果，本研究成功地證明核糖核酸酶-L能藉由降解新發現的受質，脂肪前驅細胞因子-1的信息核糖核酸，來參與脂肪新生的調控。並藉由體外(in vitro)細胞模式、電腦模擬(in silico)微陣列整合分析與動物實驗(in vivo)數據指出核糖核酸酶-L及脂肪前驅細胞因子-1基因表現量的負相關性。於此同時，人類血清中核糖核酸酶-L濃度的觀察研究，也顯示其與代謝症候群風險有顯著負相關。

Ribonuclease L (RNase-L) participates in the oligoadenylate synthetase (OAS)-RNase-L pathway in response to the infection of RNA virus. However, recently, RNase-L also has been shown to regulate various cellular functions, including myogenesis, cell proliferation, and even carcinogenesis, through its ribonuclease specificity. Herein, the aim of this thesis is to investigate the role of RNase-L in adipogenesis and metabolism.
In the beginning, it was shown that knockdown of RNase-L reduced 3T3-L1 adipocyte differentiation and lipid accumulation. When RNase-L was silenced, the expression of pre-adipocyte factor-1 (Pref-1), an early repressor in adipogenesis, and its downstream pathway, focal adhesion kinase (FAK)-extracellular signal-regulated kinases (ERK)-sex determining region Y-box 9 (Sox9), both were induced by RNase-L suppression. Hence, it was hypothesized that RNase-L destabilizes Pref-1 mRNA to influence adipogenesis. Using RT-PCR, the presence of Pref-1 mRNA was detected in the messenger ribonuleoprotein (mRNP) complexes of RNase-L precipitated with anti-RNase-L antibody. The decay rate of Pref-1 mRNA was also increased in the 3T3-L1 pre-adipocytes stably over-expressing wild-type RNase-L ribonuclease compared with that of mutants. In stable cell clones with RNase-L knockdown, the further suppression of Pref-1 mRNA by specific siRNAs can partially recover the impairment of adipocyte differentiation and lipid accumulation capacity.
Secondly, the meta-analyses of public mouse expression array data based from 11 independent studies were performed to determine the expression relationship between RNase-L and Pref-1. The robust multi-array average (RMA) algorithm utilizing statistical software R was used to normalize the expression distribution of each sample. The meta-analysis among 56 arrays showed a negative correlation between RNase-L and Pref-1 mRNA levels in murine adipose tissue. Interestingly, higher RNase-L and lower Pref-1 mRNAs were found in the adipose tissues of high-fat diet-fed rodents compared with those fed by normal diet, whether in this in silico meta-analysis or in vivo rat data.
Over the past decades, the prevalence of metabolic syndrome (MetS) has been increasing worldwide. Since RNase-L is related to adipogenesis and obesity, our next goal was to measure the serum levels of RNase-L in humans and analyze the relationship with metabolic status. An in-house enzyme-linked immunosorbent assay (ELISA) was developed to measure human serum RNase-L levels. In a total of 396 subjects, the levels of serum RNase-L of the subjects with MetS were lower than those without (16.5±6.4 μg/ml vs. 18.4±8.0 μg/ml, P=0.018). The subjects with central obesity, elevated blood pressure, or impaired fasting glucose (IFG) had significantly lower serum RNase-L levels compared with that without. Diastolic blood pressure (β=-0.124, P=0.031) and high-density lipoprotein cholesterol (HDL-C) (β=0.131, P=0.038) was related to serum RNase-L in multivariate linear regression analysis. Risk of MetS (OR, 0.83, 95% CI, 0.71-0.98, P=0.028), central obesity (OR, 0.82, 95% CI, 0.71-0.94, P=0.005), or low HDL-C (OR, 0.86, 95% CI, 0.74-1.00, P=0.042) was reduced with every 5 μg/ml increase in serum RNase-L level. Moreover, age is also inversely associated to serum RNase-L levels in various analyses.
Taken together, it was demonstrated in this thesis that RNase-L is involved in adipogenesis through destabilizing Pref-1 mRNA, a novel substrate of RNase-L. These multiple approaches, including in vitro cell model, in silico array meta-analysis, and in vivo animal data, were applied to reveal the negative relationship between RNase-L and Pref-1 in mammals. Furthermore, the negative relation between serum RNase-L levels and MetS was also proposed and look forward to better understanding the pathogenesis of the MetS.

Table of Contents
Authorization of Dissertation Committee ii
Acknowledgment iii
Abstract (Chinese) iv
Abstract vii
Table of Contents x

Main Content of Doctoral Dissertation
Chapter 1 Introduction 1
1. 1 Metabolic syndrome and adipogenesis 1
1. 1. 1 Metabolic syndrome: epidemiology and complications 1
1. 1. 2 Definition of metabolic syndrome 3
1. 1. 3 Adipogenesis and metabolic regulation of adipocytes 6
1. 1. 4 Pref-1 and pre-adipocyte stage 7
1. 1. 5 C/EBPs and adipocyte differentiation 8
1. 1. 6 PPARγ and its pharmacological agonists 10
1. 1. 7 Adipokines 11
1. 2 Roles of RNase-L 13
1. 2. 1 Biochemistry of RNase-L 13
1. 2. 2 RNase-L in antiviral innate immunity and inflammation 15
1. 2. 3 RNase-L in the regulation of cellular mRNA stability 17
1. 2. 4 RNase-L in senescence and longevity 21
1. 2. 5 Genetic association with RNASEL 21
1. 3 The purpose of this study 23
Chapter 2 Subjects, materials and methods 25
2. 1 Basic research 25
2. 1. 1 Cell culture and differentiation 25
2. 1. 2 RNase-L knockdown by lentiviral transduction 26
2. 1. 3 Oil-Red-O staining 27
2. 1. 4 RNA extraction, RT, RT-PCR, and real-time PCR 28
2. 1. 5 Western blot analysis 29
2. 1. 6 mRNP immunoprecipitation 32
2. 1. 7 Pref-1 mRNA stability assay 33
2. 1. 8 Pref-1 silencing using siRNA transfection 37
2. 1. 9 Animals and adipose tissue samples 38
2. 1. 10 Statistical analysis for lab experiments 38
2. 2 Bioinformatic research 39
2. 2. 1 Meta-analyses of mouse tissue array datasets 39
2. 2. 2 Statistical analysis 40
2. 2. 3 Gene cluster analysis 41
2. 3 Clinical research and biostatistics 41
2. 3. 1 Study subjects 41
2. 3. 2 Metabolic syndrome criteria 43
2. 3. 3 Development of the immunoassay for human serum RNase-L 44
2. 3. 4 Clinical Biostatistics 47
Chapter 3 Results 48
3. 1 RNase-L controls adipogenesis by destabilizing Pref-1 mRNA 48
3. 1. 1 IL-1β induced adiponectin downregulation in 3T3-L1 adipocytes 48
3. 1. 2 The gene expression and activity of RNase-L in adipogenesis 49
3. 1. 3 Reduced RNase-L leads to the impairment of 3T3-L1 adipocyte differentiation 50
3. 1. 4 RNase-L knockdown regulates adipogenesis-related genes 51
3. 1. 5 RNase-L knockdown induced Pref-1 downstream signaling 53
3. 1. 6 Physical association of Pref-1 mRNA with RNase-L protein 54
3. 1. 7 Pref-1 mRNA decreased with RNase-L over-expression in pre-adipocytes 55
3. 1. 8 Destabilization of Pref-1 mRNA by RNase-L ribonuclease activity 56
3. 1. 9 Silencing of Pref-1 mRNA reversed the adipogenic impairment of 3T3-L1 with RNase-L knockdown 57
3. 1. 10 The relationship between RNase-L and Pref-1 expression in mouse adipose tissues in silico 58
3. 1. 11 The relationships of RNase-L and Pref-1 or CHOP10 in mouse embryos and embryonic fibroblasts in silico 60
3. 1. 12 High-fat diet-fed rats had higher RNase-L and lower Pref-1 expression in adipose tissues in vivo 61
3. 1. 13 The relationships of RNase-L and its potential substrates in mouse adipose tissues in silico 62
3. 1. 14 In silico meta-analyses in various mouse tissues and the negative relationships of RNase-L 63
3. 1. 15 Conclusion 63
3. 2 Serum RNase-L level is inversely associated with MetS and age 64
3. 2. 1 Demographics and characteristics of human subjects in MetS study 64
3. 2. 2 Serum RNase-L levels and metabolic profiles in human subjects 65
3. 2. 3 The subjects with MetS had lower serum RNase-L levels 66
3. 2. 4 Serum RNase-L level was negatively correlated with metabolic profiles in multivariate linear regression analysis 67
3. 2. 5 Serum RNase-L level was negatively correlated with MetS diagnosis in multivariate linear regression analysis 69
3. 2. 6 To analyze the gender effect on the relation between serum RNase-L level and MetS components 70
3. 2. 7 Risk of MetS was reduced with higher serum RNase-L level 71
3. 2. 8 Correlation of insulin sensitivity and serum RNase-L level 72
3. 2. 9 Conclusion 72
Chapter 4 Discussion 74
4. 1 Biological characteristics of RNase-L 74
4. 1. 1 Role of RNase-L in adipogenesis and myogenesis 74
4. 1. 2 Roles of RNase-L in the in vivo rodent model 76
4. 1. 3 RNase-L in a view of systems biology 77
4. 2 Association between serum RNase-L and MetS 79
4. 2. 1 Age and gender effects on MetS and serum RNase-L 80
4. 2. 2 Potential mechanisms underlying the association of MetS with RNase-L 81
4. 2. 3 Extracellular RNase-L and its potential release mechanism 85
4. 3 Proposed models of RNase-L in adipose tissue and serum 87
Chapter 5 Future perspectives 90
Chapter 6 Appendix 93
References 93
Tables 107
Table 1. A partial list of adipokines. 107
Table 2. A partial list of ribonucleases. 108
Table 3. A list of potential RNase-L RNA substrates. 110
Table 4. A list of the associations between human RNASEL variants/mutation and prostate cancer risk 112
Table 5. The list of primers and probes used for RT-PCR and real-time PCR. 113
Table 6. The list of GEO sample ID (GSM) and reference series number (GSE) compiled for the meta-analysis of adipose tissues. 115
Table 7. The list of GEO sample ID (GSM) and reference series number (GSE) compiled for the meta-analyses of MEFs and embryos. 116
Table 8. The example of R script of RMA normalization for chip array meta-analysis in this study 117
Table 9. The list of the regulated genes and their expression changes by interleukin-1β treatment in 3T3-L1 adipocytes. 119
Table 10. The overlapping genes negatively correlated with RNase-L in seven various tissues 120
Table 11. The demographics, biochemical characteristics, and medical history of recruited subjects. 121
Table 12. The demographics, biochemical characteristics, and medical history of the subjects with or without metabolic syndrome (MetS). 122
Table 13. The demographics, biochemical characteristics, and medical history of the subjects between genders. 123
Table 14. Pearson’s correlation coefficients of serum RNase-L with variables. 125
Table 15. The relation between serum RNase-L and metabolic factors in multivariate linear regression analyses. 126
Table 16. The relation among serum RNase-L, metabolic factors, and treatments in multivariate linear regression analyses. 128
Table 17. The relation between serum RNase-L and the diagnosis of MetS in multivariate linear regression analyses. 129
Table 18. The odds ratios for MetS according to the different quantities of serum RNase-L increase. 130
Table 19. The odds ratios for MetS and components according to every 5 μg/ml serum RNase-L increase. 131
Table 20. The odds ratios for MetS and components according to every 5 μg/ml serum RNase-L increase categorized by gender. 132
Figures 133
Figure 1 133
Figure 2 135
Figure 3 136
Figure 4 137
Figure 5 138
Figure 6 140
Figure 7 142
Figure 8 144
Figure 9 146
Figure 10 148
Figure 11 150
Figure 12 152
Figure 13 154
Figure 14 156
Figure 15 157
Figure 16 159
Figure 17 160
Figure 18 161
Figure 19 162
Figure 20 163
Figure 21 164
Publications 165

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